Udemy PyTorch for Deep Learning in 2023 Zero to Mastery
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Udemy PyTorch for Deep Learning in 2023 Zero to Mastery
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Udemy PyTorch for Deep Learning in 2023 Zero to Mastery
[CourseClub.Me].url -
[FreeCourseSite.com].url -
[GigaCourse.Com].url -
1. PyTorch for Deep Learning.mp4 -
1. PyTorch for Deep Learning.srt -
2. Course Welcome and What Is Deep Learning.mp4 -
2. Course Welcome and What Is Deep Learning.srt -
3. Join Our Online Classroom!.mp4 -
3. Join Our Online Classroom!.srt -
4. Exercise Meet Your Classmates + Instructor.html -
5. Free Course Book + Code Resources + Asking Questions + Getting Help.html -
6. ZTM Resources.mp4 -
6. ZTM Resources.srt -
6.1 LinkedIn Group.html -
6.2 zerotomastery.io.html -
6.3 ZTM Youtube.html -
7. Machine Learning + Python Monthly Newsletters.html -
1. What Is a Machine Learning Research Paper.mp4 -
1. What Is a Machine Learning Research Paper.srt -
10. Breaking Down Figure 1 of the ViT Paper.mp4 -
10. Breaking Down Figure 1 of the ViT Paper.srt -
11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 -
11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt -
12. Breaking Down Equation 1.mp4 -
12. Breaking Down Equation 1.srt -
13. Breaking Down Equation 2 and 3.mp4 -
13. Breaking Down Equation 2 and 3.srt -
14. Breaking Down Equation 4.mp4 -
14. Breaking Down Equation 4.srt -
15. Breaking Down Table 1.mp4 -
15. Breaking Down Table 1.srt -
16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 -
16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt -
17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 -
17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt -
18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 -
18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt -
19. Creating Patch Embeddings with a Convolutional Layer.mp4 -
19. Creating Patch Embeddings with a Convolutional Layer.srt -
2. Why Replicate a Machine Learning Research Paper.mp4 -
2. Why Replicate a Machine Learning Research Paper.srt -
20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 -
20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt -
21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 -
21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt -
22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 -
22. Visualizing a Single Sequence Vector of Patch Embeddings.srt -
23. Creating the Patch Embedding Layer with PyTorch.mp4 -
23. Creating the Patch Embedding Layer with PyTorch.srt -
24. Creating the Class Token Embedding.mp4 -
24. Creating the Class Token Embedding.srt -
25. Creating the Class Token Embedding - Less Birds.mp4 -
25. Creating the Class Token Embedding - Less Birds.srt -
26. Creating the Position Embedding.mp4 -
26. Creating the Position Embedding.srt -
27. Equation 1 Putting it All Together.mp4 -
27. Equation 1 Putting it All Together.srt -
28. Equation 2 Multihead Attention Overview.mp4 -
28. Equation 2 Multihead Attention Overview.srt -
29. Equation 2 Layernorm Overview.mp4 -
29. Equation 2 Layernorm Overview.srt -
3. Where Can You Find Machine Learning Research Papers and Code.mp4 -
3. Where Can You Find Machine Learning Research Papers and Code.srt -
30. Turning Equation 2 into Code.mp4 -
30. Turning Equation 2 into Code.srt -
31. Checking the Inputs and Outputs of Equation.mp4 -
31. Checking the Inputs and Outputs of Equation.srt -
32. Equation 3 Replication Overview.mp4 -
32. Equation 3 Replication Overview.srt -
33. Turning Equation 3 into Code.mp4 -
33. Turning Equation 3 into Code.srt -
34. Transformer Encoder Overview.mp4 -
34. Transformer Encoder Overview.srt -
35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 -
35. Combining equation 2 and 3 to Create the Transformer Encoder.srt -
36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 -
36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt -
37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 -
37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.srt -
38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 -
38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.srt -
39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 -
39. Getting a Visual Summary of Our Custom Vision Transformer.srt -
4. What We Are Going to Cover.mp4 -
4. What We Are Going to Cover.srt -
40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 -
40. Creating a Loss Function and Optimizer from the ViT Paper.srt -
41. Training our Custom ViT on Food Vision Mini.mp4 -
41. Training our Custom ViT on Food Vision Mini.srt -
42. Discussing what Our Training Setup Is Missing.mp4 -
42. Discussing what Our Training Setup Is Missing.srt -
43. Plotting a Loss Curve for Our ViT Model.mp4 -
43. Plotting a Loss Curve for Our ViT Model.srt -
44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 -
44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt -
45. Preparing Data to Be Used with a Pretrained ViT.mp4 -
45. Preparing Data to Be Used with a Pretrained ViT.srt -
46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 -
46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt -
47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 -
47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt -
48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 -
48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt -
49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 -
49. Making Predictions on a Custom Image with Our Pretrained ViT.srt -
5. Getting Setup for Coding in Google Colab.mp4 -
5. Getting Setup for Coding in Google Colab.srt -
50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 -
50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt -
6. Downloading Data for Food Vision Mini.mp4 -
6. Downloading Data for Food Vision Mini.srt -
7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 -
7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt -
8. Visualizing a Single Image.mp4 -
8. Visualizing a Single Image.srt -
9. Replicating a Vision Transformer - High Level Overview.mp4 -
9. Replicating a Vision Transformer - High Level Overview.srt -
1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 -
1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.srt -
10. Creating an EffNetB2 Feature Extractor Model.mp4 -
10. Creating an EffNetB2 Feature Extractor Model.srt -
11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 -
11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt -
12. Creating DataLoaders for EffNetB2.mp4 -
12. Creating DataLoaders for EffNetB2.srt -
13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 -
13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt -
14. Saving Our EffNetB2 Model to File.mp4 -
14. Saving Our EffNetB2 Model to File.srt -
15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 -
15. Getting the Size of Our EffNetB2 Model in Megabytes.srt -
16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 -
16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt -
17. Creating a Vision Transformer Feature Extractor Model.mp4 -
17. Creating a Vision Transformer Feature Extractor Model.srt -
18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 -
18. Creating DataLoaders for Our ViT Feature Extractor Model.srt -
19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 -
19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt -
2. Three Questions to Ask for Machine Learning Model Deployment.mp4 -
2. Three Questions to Ask for Machine Learning Model Deployment.srt -
20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 -
20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt -
21. Collecting Stats About Our-ViT Feature Extractor.mp4 -
21. Collecting Stats About Our-ViT Feature Extractor.srt -
22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 -
22. Outlining the Steps for Making and Timing Predictions for Our Models.srt -
23. Creating a Function to Make and Time Predictions with Our Models.mp4 -
23. Creating a Function to Make and Time Predictions with Our Models.srt -
24. Making and Timing Predictions with EffNetB2.mp4 -
24. Making and Timing Predictions with EffNetB2.srt -
25. Making and Timing Predictions with ViT.mp4 -
25. Making and Timing Predictions with ViT.srt -
26. Comparing EffNetB2 and ViT Model Statistics.mp4 -
26. Comparing EffNetB2 and ViT Model Statistics.srt -
27. Visualizing the Performance vs Speed Trade-off.mp4 -
27. Visualizing the Performance vs Speed Trade-off.srt -
28. Gradio Overview and Installation.mp4 -
28. Gradio Overview and Installation.srt -
29. Gradio Function Outline.mp4 -
29. Gradio Function Outline.srt -
3. Where Is My Model Going to Go.mp4 -
3. Where Is My Model Going to Go.srt -
30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 -
30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt -
31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 -
31. Creating a List of Examples to Pass to Our Gradio Demo.srt -
32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 -
32. Bringing Food Vision Mini to Life in a Live Web Application.srt -
33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 -
33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt -
34. Outlining the File Structure of Our Deployed App.mp4 -
34. Outlining the File Structure of Our Deployed App.srt -
35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 -
35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt -
36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 -
36. Creating an Examples Directory with Example Food Vision Mini Images.srt -
37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 -
37. Writing Code to Move Our Saved EffNetB2 Model File.srt -
38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 -
38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt -
39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 -
39. Turning Our Food Vision Mini Demo App Into a Python Script.srt -
4. How Is My Model Going to Function.mp4 -
4. How Is My Model Going to Function.srt -
40. Creating a Requirements File for Our Food Vision Mini App.mp4 -
40. Creating a Requirements File for Our Food Vision Mini App.srt -
41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 -
41. Downloading Our Food Vision Mini App Files from Google Colab.srt -
42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 -
42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt -
43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 -
43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt -
44. Food Vision Big Project Outline.mp4 -
44. Food Vision Big Project Outline.srt -
45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 -
45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt -
46. Downloading the Food 101 Dataset.mp4 -
46. Downloading the Food 101 Dataset.srt -
47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 -
47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt -
48. Turning Our Food 101 Datasets into DataLoaders.mp4 -
48. Turning Our Food 101 Datasets into DataLoaders.srt -
49. Training Food Vision Big Our Biggest Model Yet!.mp4 -
49. Training Food Vision Big Our Biggest Model Yet!.srt -
5. Some Tools and Places to Deploy Machine Learning Models.mp4 -
5. Some Tools and Places to Deploy Machine Learning Models.srt -
50. Outlining the File Structure for Our Food Vision Big.mp4 -
50. Outlining the File Structure for Our Food Vision Big.srt -
51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 -
51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt -
52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 -
52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt -
53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 -
53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt -
54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 -
54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt -
55. Zipping and Downloading Our Food Vision Big App Files.mp4 -
55. Zipping and Downloading Our Food Vision Big App Files.srt -
56. Deploying Food Vision Big to Hugging Face Spaces.mp4 -
56. Deploying Food Vision Big to Hugging Face Spaces.srt -
57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 -
57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt -
6. What We Are Going to Cover.mp4 -
6. What We Are Going to Cover.srt -
7. Getting Setup to Code.mp4 -
7. Getting Setup to Code.srt -
8. Downloading a Dataset for Food Vision Mini.mp4 -
8. Downloading a Dataset for Food Vision Mini.srt -
9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 -
9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt -
[CourseClub.Me].url -
[FreeCourseSite.com].url -
[GigaCourse.Com].url -
1. Introduction to PyTorch 2.0.mp4 -
1. Introduction to PyTorch 2.0.srt -
10. Creating a Function to Setup Our Model and Transforms.mp4 -
10. Creating a Function to Setup Our Model and Transforms.srt -
11. Discussing How to Get Better Relative Speedups for Training Models.mp4 -
11. Discussing How to Get Better Relative Speedups for Training Models.srt -
12. Setting the Batch Size and Data Size Programmatically.mp4 -
12. Setting the Batch Size and Data Size Programmatically.srt -
13. Getting More Potential Speedups with TensorFloat-32.mp4 -
13. Getting More Potential Speedups with TensorFloat-32.srt -
14. Downloading the CIFAR10 Dataset.mp4 -
14. Downloading the CIFAR10 Dataset.srt -
15. Creating Training and Test DataLoaders.mp4 -
15. Creating Training and Test DataLoaders.srt -
16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp4 -
16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.srt -
17. Experiment 1 - Single Run without torch.compile.mp4 -
17. Experiment 1 - Single Run without torch.compile.srt -
18. Experiment 2 - Single Run with torch.compile.mp4 -
18. Experiment 2 - Single Run with torch.compile.srt -
19. Comparing the Results of Experiment 1 and 2.mp4 -
19. Comparing the Results of Experiment 1 and 2.srt -
2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 -
2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt -
2.1 PyTorch 2.0 tutorial on learnpytorch.io.html -
20. Saving the Results of Experiment 1 and 2.mp4 -
20. Saving the Results of Experiment 1 and 2.srt -
21. Preparing Functions for Experiment 3 and 4.mp4 -
21. Preparing Functions for Experiment 3 and 4.srt -
22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 -
22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt -
23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 -
23. Experiment 4 - Training a Compiled Model for Multiple Runs.srt -
24. Comparing the Results of Experiment 3 and 4.mp4 -
24. Comparing the Results of Experiment 3 and 4.srt -
25. Potential Extensions and Resources to Learn More.mp4 -
25. Potential Extensions and Resources to Learn More.srt -
3. Getting Started with PyTorch 2 in Google Colab.mp4 -
3. Getting Started with PyTorch 2 in Google Colab.srt -
3.1 PyTorch 2.0 tutorial on learnpytorch.io.html -
4. PyTorch 2.0 - 30 Second Intro.mp4 -
4. PyTorch 2.0 - 30 Second Intro.srt -
5. Getting Setup for PyTorch 2.mp4 -
5. Getting Setup for PyTorch 2.srt -
6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 -
6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.srt -
7. Setting the Default Device in PyTorch 2.mp4 -
7. Setting the Default Device in PyTorch 2.srt -
8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 -
8. Discussing the Experiments We Are Going to Run for PyTorch 2.srt -
9. Introduction to PyTorch 2.mp4 -
9. Introduction to PyTorch 2.srt -
1. Special Bonus Lecture.html -
1. Thank You!.mp4 -
1. Thank You!.srt -
2. Become An Alumni.html -
3. Endorsements on LinkedIn.html -
4. Learning Guideline.html -
1. Why Use Machine Learning or Deep Learning.mp4 -
1. Why Use Machine Learning or Deep Learning.srt -
10. How To and How Not To Approach This Course.mp4 -
10. How To and How Not To Approach This Course.srt -
11. Important Resources For This Course.mp4 -
11. Important Resources For This Course.srt -
12. Getting Setup to Write PyTorch Code.mp4 -
12. Getting Setup to Write PyTorch Code.srt -
13. Introduction to PyTorch Tensors.mp4 -
13. Introduction to PyTorch Tensors.srt -
14. Creating Random Tensors in PyTorch.mp4 -
14. Creating Random Tensors in PyTorch.srt -
15. Creating Tensors With Zeros and Ones in PyTorch.mp4 -
15. Creating Tensors With Zeros and Ones in PyTorch.srt -
16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 -
16. Creating a Tensor Range and Tensors Like Other Tensors.srt -
17. Dealing With Tensor Data Types.mp4 -
17. Dealing With Tensor Data Types.srt -
18. Getting Tensor Attributes.mp4 -
18. Getting Tensor Attributes.srt -
19. Manipulating Tensors (Tensor Operations).mp4 -
19. Manipulating Tensors (Tensor Operations).srt -
2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 -
2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt -
20. Matrix Multiplication (Part 1).mp4 -
20. Matrix Multiplication (Part 1).srt -
21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 -
21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt -
22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 -
22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt -
23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 -
23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt -
24. Finding The Positional Min and Max of Tensors.mp4 -
24. Finding The Positional Min and Max of Tensors.srt -
25. Reshaping, Viewing and Stacking Tensors.mp4 -
25. Reshaping, Viewing and Stacking Tensors.srt -
26. Squeezing, Unsqueezing and Permuting Tensors.mp4 -
26. Squeezing, Unsqueezing and Permuting Tensors.srt -
27. Selecting Data From Tensors (Indexing).mp4 -
27. Selecting Data From Tensors (Indexing).srt -
28. PyTorch Tensors and NumPy.mp4 -
28. PyTorch Tensors and NumPy.srt -
29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 -
29. PyTorch Reproducibility (Taking the Random Out of Random).srt -
3. Machine Learning vs. Deep Learning.mp4 -
3. Machine Learning vs. Deep Learning.srt -
30. Different Ways of Accessing a GPU in PyTorch.mp4 -
30. Different Ways of Accessing a GPU in PyTorch.srt -
31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 -
31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.srt -
32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 -
32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt -
4. Anatomy of Neural Networks.mp4 -
4. Anatomy of Neural Networks.srt -
5. Different Types of Learning Paradigms.mp4 -
5. Different Types of Learning Paradigms.srt -
6. What Can Deep Learning Be Used For.mp4 -
6. What Can Deep Learning Be Used For.srt -
7. What Is and Why PyTorch.mp4 -
7. What Is and Why PyTorch.srt -
8. What Are Tensors.mp4 -
8. What Are Tensors.srt -
9. What We Are Going To Cover With PyTorch.mp4 -
9. What We Are Going To Cover With PyTorch.srt -
1. Introduction and Where You Can Get Help.mp4 -
1. Introduction and Where You Can Get Help.srt -
10. Making Predictions With Our Random Model Using Inference Mode.mp4 -
10. Making Predictions With Our Random Model Using Inference Mode.srt -
11. Training a Model Intuition (The Things We Need).mp4 -
11. Training a Model Intuition (The Things We Need).srt -
12. Setting Up an Optimizer and a Loss Function.mp4 -
12. Setting Up an Optimizer and a Loss Function.srt -
13. PyTorch Training Loop Steps and Intuition.mp4 -
13. PyTorch Training Loop Steps and Intuition.srt -
14. Writing Code for a PyTorch Training Loop.mp4 -
14. Writing Code for a PyTorch Training Loop.srt -
15. Reviewing the Steps in a Training Loop Step by Step.mp4 -
15. Reviewing the Steps in a Training Loop Step by Step.srt -
16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 -
16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt -
17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 -
17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt -
18. Reviewing What Happens in a Testing Loop Step by Step.mp4 -
18. Reviewing What Happens in a Testing Loop Step by Step.srt -
19. Writing Code to Save a PyTorch Model.mp4 -
19. Writing Code to Save a PyTorch Model.srt -
2. Getting Setup and What We Are Covering.mp4 -
2. Getting Setup and What We Are Covering.srt -
20. Writing Code to Load a PyTorch Model.mp4 -
20. Writing Code to Load a PyTorch Model.srt -
21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 -
21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.srt -
22. Putting Everything Together (Part 1) Data.mp4 -
22. Putting Everything Together (Part 1) Data.srt -
23. Putting Everything Together (Part 2) Building a Model.mp4 -
23. Putting Everything Together (Part 2) Building a Model.srt -
24. Putting Everything Together (Part 3) Training a Model.mp4 -
24. Putting Everything Together (Part 3) Training a Model.srt -
25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 -
25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt -
26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 -
26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt -
27. Exercise Imposter Syndrome.mp4 -
27. Exercise Imposter Syndrome.srt -
28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 -
28. PyTorch Workflow Exercises and Extra-Curriculum.srt -
3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 -
3. Creating a Simple Dataset Using the Linear Regression Formula.srt -
4. Splitting Our Data Into Training and Test Sets.mp4 -
4. Splitting Our Data Into Training and Test Sets.srt -
5. Building a function to Visualize Our Data.mp4 -
5. Building a function to Visualize Our Data.srt -
6. Creating Our First PyTorch Model for Linear Regression.mp4 -
6. Creating Our First PyTorch Model for Linear Regression.srt -
7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 -
7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt -
8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 -
8. Discussing Some of the Most Important PyTorch Model Building Classes.srt -
9. Checking Out the Internals of Our PyTorch Model.mp4 -
9. Checking Out the Internals of Our PyTorch Model.srt -
1. Introduction to Machine Learning Classification With PyTorch.mp4 -
1. Introduction to Machine Learning Classification With PyTorch.srt -
10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 -
10. Loss Function Optimizer and Evaluation Function for Our Classification Network.srt -
11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 -
11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt -
12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 -
12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt -
13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 -
13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt -
14. Discussing Options to Improve a Model.mp4 -
14. Discussing Options to Improve a Model.srt -
15. Creating a New Model with More Layers and Hidden Units.mp4 -
15. Creating a New Model with More Layers and Hidden Units.srt -
16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 -
16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.srt -
17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 -
17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt -
18. Building and Training a Model to Fit on Straight Line Data.mp4 -
18. Building and Training a Model to Fit on Straight Line Data.srt -
19. Evaluating Our Models Predictions on Straight Line Data.mp4 -
19. Evaluating Our Models Predictions on Straight Line Data.srt -
2. Classification Problem Example Input and Output Shapes.mp4 -
2. Classification Problem Example Input and Output Shapes.srt -
20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 -
20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt -
21. Building Our First Neural Network with Non-Linearity.mp4 -
21. Building Our First Neural Network with Non-Linearity.srt -
22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 -
22. Writing Training and Testing Code for Our First Non-Linear Model.srt -
23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 -
23. Making Predictions with and Evaluating Our First Non-Linear Model.srt -
24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 -
24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt -
25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 -
25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt -
26. Creating a Multi-Class Classification Model with PyTorch.mp4 -
26. Creating a Multi-Class Classification Model with PyTorch.srt -
27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 -
27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt -
28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 -
28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt -
29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 -
29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt -
3. Typical Architecture of a Classification Neural Network (Overview).mp4 -
3. Typical Architecture of a Classification Neural Network (Overview).srt -
30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 -
30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt -
31. Discussing a Few More Classification Metrics.mp4 -
31. Discussing a Few More Classification Metrics.srt -
32. PyTorch Classification Exercises and Extra-Curriculum.mp4 -
32. PyTorch Classification Exercises and Extra-Curriculum.srt -
4. Making a Toy Classification Dataset.mp4 -
4. Making a Toy Classification Dataset.srt -
5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 -
5. Turning Our Data into Tensors and Making a Training and Test Split.srt -
6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 -
6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt -
7. Coding a Small Neural Network to Handle Our Classification Data.mp4 -
7. Coding a Small Neural Network to Handle Our Classification Data.srt -
8. Making Our Neural Network Visual.mp4 -
8. Making Our Neural Network Visual.srt -
9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 -
9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt -
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1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 -
1. What Is a Computer Vision Problem and What We Are Going to Cover.srt -
10. Creating a Loss Function an Optimizer for Model 0.mp4 -
10. Creating a Loss Function an Optimizer for Model 0.srt -
11. Creating a Function to Time Our Modelling Code.mp4 -
11. Creating a Function to Time Our Modelling Code.srt -
12. Writing Training and Testing Loops for Our Batched Data.mp4 -
12. Writing Training and Testing Loops for Our Batched Data.srt -
13. Writing an Evaluation Function to Get Our Models Results.mp4 -
13. Writing an Evaluation Function to Get Our Models Results.srt -
14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 -
14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt -
15. Model 1 Creating a Model with Non-Linear Functions.mp4 -
15. Model 1 Creating a Model with Non-Linear Functions.srt -
16. Mode 1 Creating a Loss Function and Optimizer.mp4 -
16. Mode 1 Creating a Loss Function and Optimizer.srt -
17. Turing Our Training Loop into a Function.mp4 -
17. Turing Our Training Loop into a Function.srt -
18. Turing Our Testing Loop into a Function.mp4 -
18. Turing Our Testing Loop into a Function.srt -
19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 -
19. Training and Testing Model 1 with Our Training and Testing Functions.srt -
2. Computer Vision Input and Output Shapes.mp4 -
2. Computer Vision Input and Output Shapes.srt -
20. Getting a Results Dictionary for Model 1.mp4 -
20. Getting a Results Dictionary for Model 1.srt -
21. Model 2 Convolutional Neural Networks High Level Overview.mp4 -
21. Model 2 Convolutional Neural Networks High Level Overview.srt -
22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 -
22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt -
23. Model 2 Breaking Down Conv2D Step by Step.mp4 -
23. Model 2 Breaking Down Conv2D Step by Step.srt -
24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 -
24. Model 2 Breaking Down MaxPool2D Step by Step.srt -
25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 -
25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt -
26. Model 2 Setting Up a Loss Function and Optimizer.mp4 -
26. Model 2 Setting Up a Loss Function and Optimizer.srt -
27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 -
27. Model 2 Training Our First CNN and Evaluating Its Results.srt -
28. Comparing the Results of Our Modelling Experiments.mp4 -
28. Comparing the Results of Our Modelling Experiments.srt -
29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 -
29. Making Predictions on Random Test Samples with the Best Trained Model.srt -
3. What Is a Convolutional Neural Network (CNN).mp4 -
3. What Is a Convolutional Neural Network (CNN).srt -
30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 -
30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt -
31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 -
31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.srt -
32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 -
32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt -
33. Saving and Loading Our Best Performing Model.mp4 -
33. Saving and Loading Our Best Performing Model.srt -
34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 -
34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt -
4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 -
4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt -
5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 -
5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt -
6. Visualizing Random Samples of Data.mp4 -
6. Visualizing Random Samples of Data.srt -
7. DataLoader Overview Understanding Mini-Batches.mp4 -
7. DataLoader Overview Understanding Mini-Batches.srt -
8. Turning Our Datasets Into DataLoaders.mp4 -
8. Turning Our Datasets Into DataLoaders.srt -
9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 -
9. Model 0 Creating a Baseline Model with Two Linear Layers.srt -
1. What Is a Custom Dataset and What We Are Going to Cover.mp4 -
1. What Is a Custom Dataset and What We Are Going to Cover.srt -
10. Visualizing a Loaded Image From the Train Dataset.mp4 -
10. Visualizing a Loaded Image From the Train Dataset.srt -
11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 -
11. Turning Our Image Datasets into PyTorch Dataloaders.srt -
12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 -
12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt -
13. Creating a Helper Function to Get Class Names From a Directory.mp4 -
13. Creating a Helper Function to Get Class Names From a Directory.srt -
14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 -
14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt -
15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 -
15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.srt -
16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 -
16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt -
17. Turning Our Custom Datasets Into DataLoaders.mp4 -
17. Turning Our Custom Datasets Into DataLoaders.srt -
18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 -
18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt -
19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 -
19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt -
2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 -
2. Importing PyTorch and Setting Up Device Agnostic Code.srt -
20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 -
20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt -
21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 -
21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.srt -
22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 -
22. Using the Torchinfo Package to Get a Summary of Our Model.srt -
23. Creating Training and Testing loop Functions.mp4 -
23. Creating Training and Testing loop Functions.srt -
24. Creating a Train Function to Train and Evaluate Our Models.mp4 -
24. Creating a Train Function to Train and Evaluate Our Models.srt -
25. Training and Evaluating Model 0 With Our Training Functions.mp4 -
25. Training and Evaluating Model 0 With Our Training Functions.srt -
26. Plotting the Loss Curves of Model 0.mp4 -
26. Plotting the Loss Curves of Model 0.srt -
27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 -
27. The Balance Between Overfitting and Underfitting and How to Deal With Each.srt -
28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 -
28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt -
29. Constructing and Training Model 1.mp4 -
29. Constructing and Training Model 1.srt -
3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 -
3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt -
30. Plotting the Loss Curves of Model 1.mp4 -
30. Plotting the Loss Curves of Model 1.srt -
31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 -
31. Plotting the Loss Curves of All of Our Models Against Each Other.srt -
32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 -
32. Predicting on Custom Data (Part 1) Downloading an Image.srt -
33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 -
33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.srt -
34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 -
34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.srt -
35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 -
35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.srt -
36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 -
36. Predicting on Custom Data (Part 5) Putting It All Together.srt -
37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 -
37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt -
4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 -
4. Becoming One With the Data (Part 1) Exploring the Data Format.srt -
5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 -
5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt -
6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 -
6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt -
7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 -
7. Transforming Data (Part 1) Turning Images Into Tensors.srt -
8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 -
8. Transforming Data (Part 2) Visualizing Transformed Images.srt -
9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 -
9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt -
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1. What Is Going Modular and What We Are Going to Cover.mp4 -
1. What Is Going Modular and What We Are Going to Cover.srt -
10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 -
10. Going Modular Summary, Exercises and Extra-Curriculum.srt -
2. Going Modular Notebook (Part 1) Running It End to End.mp4 -
2. Going Modular Notebook (Part 1) Running It End to End.srt -
3. Downloading a Dataset.mp4 -
3. Downloading a Dataset.srt -
4. Writing the Outline for Our First Python Script to Setup the Data.mp4 -
4. Writing the Outline for Our First Python Script to Setup the Data.srt -
5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 -
5. Creating a Python Script to Create Our PyTorch DataLoaders.srt -
6. Turning Our Model Building Code into a Python Script.mp4 -
6. Turning Our Model Building Code into a Python Script.srt -
7. Turning Our Model Training Code into a Python Script.mp4 -
7. Turning Our Model Training Code into a Python Script.srt -
8. Turning Our Utility Function to Save a Model into a Python Script.mp4 -
8. Turning Our Utility Function to Save a Model into a Python Script.srt -
9. Creating a Training Script to Train Our Model in One Line of Code.mp4 -
9. Creating a Training Script to Train Our Model in One Line of Code.srt -
1. Introduction What is Transfer Learning and Why Use It.mp4 -
1. Introduction What is Transfer Learning and Why Use It.srt -
10. Different Kinds of Transfer Learning.mp4 -
10. Different Kinds of Transfer Learning.srt -
11. Getting a Summary of the Different Layers of Our Model.mp4 -
11. Getting a Summary of the Different Layers of Our Model.srt -
12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 -
12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt -
13. Training Our First Transfer Learning Feature Extractor Model.mp4 -
13. Training Our First Transfer Learning Feature Extractor Model.srt -
14. Plotting the Loss curves of Our Transfer Learning Model.mp4 -
14. Plotting the Loss curves of Our Transfer Learning Model.srt -
15. Outlining the Steps to Make Predictions on the Test Images.mp4 -
15. Outlining the Steps to Make Predictions on the Test Images.srt -
16. Creating a Function Predict On and Plot Images.mp4 -
16. Creating a Function Predict On and Plot Images.srt -
17. Making and Plotting Predictions on Test Images.mp4 -
17. Making and Plotting Predictions on Test Images.srt -
18. Making a Prediction on a Custom Image.mp4 -
18. Making a Prediction on a Custom Image.srt -
19. Main Takeaways, Exercises and Extra- Curriculum.mp4 -
19. Main Takeaways, Exercises and Extra- Curriculum.srt -
2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 -
2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt -
3. Installing the Latest Versions of Torch and Torchvision.mp4 -
3. Installing the Latest Versions of Torch and Torchvision.srt -
4. Downloading Our Previously Written Code from Going Modular.mp4 -
4. Downloading Our Previously Written Code from Going Modular.srt -
5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 -
5. Downloading Pizza, Steak, Sushi Image Data from Github.srt -
6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 -
6. Turning Our Data into DataLoaders with Manually Created Transforms.srt -
7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 -
7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt -
8. Which Pretrained Model Should You Use.mp4 -
8. Which Pretrained Model Should You Use.srt -
9. Setting Up a Pretrained Model with Torchvision.mp4 -
9. Setting Up a Pretrained Model with Torchvision.srt -
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1. What Is Experiment Tracking and Why Track Experiments.mp4 -
1. What Is Experiment Tracking and Why Track Experiments.srt -
10. Creating a Function to Create SummaryWriter Instances.mp4 -
10. Creating a Function to Create SummaryWriter Instances.srt -
11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 -
11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt -
12. What Experiments Should You Try.mp4 -
12. What Experiments Should You Try.srt -
13. Discussing the Experiments We Are Going to Try.mp4 -
13. Discussing the Experiments We Are Going to Try.srt -
14. Downloading Datasets for Our Modelling Experiments.mp4 -
14. Downloading Datasets for Our Modelling Experiments.srt -
15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 -
15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt -
16. Creating Functions to Prepare Our Feature Extractor Models.mp4 -
16. Creating Functions to Prepare Our Feature Extractor Models.srt -
17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 -
17. Coding Out the Steps to Run a Series of Modelling Experiments.srt -
18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 -
18. Running Eight Different Modelling Experiments in 5 Minutes.srt -
19. Viewing Our Modelling Experiments in TensorBoard.mp4 -
19. Viewing Our Modelling Experiments in TensorBoard.srt -
2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 -
2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt -
20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 -
20. Loading the Best Model and Making Predictions on Random Images from the Test Set.srt -
21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 -
21. Making a Prediction on Our Own Custom Image with the Best Model.srt -
22. Main Takeaways, Exercises and Extra- Curriculum.mp4 -
22. Main Takeaways, Exercises and Extra- Curriculum.srt -
3. Creating a Function to Download Data.mp4 -
3. Creating a Function to Download Data.srt -
4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 -
4. Turning Our Data into DataLoaders Using Manual Transforms.srt -
5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 -
5. Turning Our Data into DataLoaders Using Automatic Transforms.srt -
6. Preparing a Pretrained Model for Our Own Problem.mp4 -
6. Preparing a Pretrained Model for Our Own Problem.srt -
7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 -
7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.srt -
8. Training a Single Model and Saving the Results to TensorBoard.mp4 -
8. Training a Single Model and Saving the Results to TensorBoard.srt -
9. Exploring Our Single Models Results with TensorBoard.mp4 -
9. Exploring Our Single Models Results with TensorBoard.srt -
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[CourseClub.Me].url -
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1. PyTorch for Deep Learning.mp4 -
75.3 MB
1. PyTorch for Deep Learning.srt -
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2. Course Welcome and What Is Deep Learning.mp4 -
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2. Course Welcome and What Is Deep Learning.srt -
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3. Join Our Online Classroom!.mp4 -
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4. Exercise Meet Your Classmates + Instructor.html -
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5. Free Course Book + Code Resources + Asking Questions + Getting Help.html -
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6. ZTM Resources.mp4 -
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6.1 LinkedIn Group.html -
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6.2 zerotomastery.io.html -
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6.3 ZTM Youtube.html -
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7. Machine Learning + Python Monthly Newsletters.html -
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1. What Is a Machine Learning Research Paper.mp4 -
93.9 MB
1. What Is a Machine Learning Research Paper.srt -
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10. Breaking Down Figure 1 of the ViT Paper.mp4 -
87.1 MB
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11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 -
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12. Breaking Down Equation 1.mp4 -
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13. Breaking Down Equation 2 and 3.mp4 -
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15. Breaking Down Table 1.mp4 -
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16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 -
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17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 -
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18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 -
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19. Creating Patch Embeddings with a Convolutional Layer.mp4 -
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2. Why Replicate a Machine Learning Research Paper.mp4 -
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20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 -
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21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 -
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22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 -
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23. Creating the Patch Embedding Layer with PyTorch.mp4 -
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24. Creating the Class Token Embedding.mp4 -
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25. Creating the Class Token Embedding - Less Birds.mp4 -
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26. Creating the Position Embedding.mp4 -
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3. Where Can You Find Machine Learning Research Papers and Code.mp4 -
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34. Transformer Encoder Overview.mp4 -
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35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 -
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36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 -
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37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 -
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38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 -
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39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 -
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4. What We Are Going to Cover.mp4 -
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40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 -
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41. Training our Custom ViT on Food Vision Mini.mp4 -
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42. Discussing what Our Training Setup Is Missing.mp4 -
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43. Plotting a Loss Curve for Our ViT Model.mp4 -
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44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 -
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45. Preparing Data to Be Used with a Pretrained ViT.mp4 -
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46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 -
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47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 -
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48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 -
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49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 -
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5. Getting Setup for Coding in Google Colab.mp4 -
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50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 -
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6. Downloading Data for Food Vision Mini.mp4 -
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7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 -
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8. Visualizing a Single Image.mp4 -
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9. Replicating a Vision Transformer - High Level Overview.mp4 -
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1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 -
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10. Creating an EffNetB2 Feature Extractor Model.mp4 -
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11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 -
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12. Creating DataLoaders for EffNetB2.mp4 -
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13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 -
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14. Saving Our EffNetB2 Model to File.mp4 -
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15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 -
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16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 -
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17. Creating a Vision Transformer Feature Extractor Model.mp4 -
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18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 -
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19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 -
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2. Three Questions to Ask for Machine Learning Model Deployment.mp4 -
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20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 -
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21. Collecting Stats About Our-ViT Feature Extractor.mp4 -
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22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 -
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23. Creating a Function to Make and Time Predictions with Our Models.mp4 -
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24. Making and Timing Predictions with EffNetB2.mp4 -
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24. Making and Timing Predictions with EffNetB2.srt -
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25. Making and Timing Predictions with ViT.mp4 -
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26. Comparing EffNetB2 and ViT Model Statistics.mp4 -
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27. Visualizing the Performance vs Speed Trade-off.mp4 -
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28. Gradio Overview and Installation.mp4 -
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29. Gradio Function Outline.mp4 -
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3. Where Is My Model Going to Go.mp4 -
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30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 -
95.2 MB
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31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 -
53.3 MB
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32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 -
135.4 MB
32. Bringing Food Vision Mini to Life in a Live Web Application.srt -
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33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 -
64.8 MB
33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt -
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34. Outlining the File Structure of Our Deployed App.mp4 -
89.5 MB
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35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 -
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36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 -
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36. Creating an Examples Directory with Example Food Vision Mini Images.srt -
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37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 -
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38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 -
44.8 MB
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39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 -
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4. How Is My Model Going to Function.mp4 -
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40. Creating a Requirements File for Our Food Vision Mini App.mp4 -
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40. Creating a Requirements File for Our Food Vision Mini App.srt -
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41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 -
112.2 MB
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42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 -
143.6 MB
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43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 -
91.6 MB
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44. Food Vision Big Project Outline.mp4 -
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44. Food Vision Big Project Outline.srt -
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45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 -
96.5 MB
45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt -
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46. Downloading the Food 101 Dataset.mp4 -
71.7 MB
46. Downloading the Food 101 Dataset.srt -
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47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 -
119.7 MB
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48. Turning Our Food 101 Datasets into DataLoaders.mp4 -
61.5 MB
48. Turning Our Food 101 Datasets into DataLoaders.srt -
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49. Training Food Vision Big Our Biggest Model Yet!.mp4 -
184.2 MB
49. Training Food Vision Big Our Biggest Model Yet!.srt -
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5. Some Tools and Places to Deploy Machine Learning Models.mp4 -
65.4 MB
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50. Outlining the File Structure for Our Food Vision Big.mp4 -
52.8 MB
50. Outlining the File Structure for Our Food Vision Big.srt -
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51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 -
36.6 MB
51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt -
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52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 -
66.8 MB
52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt -
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53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 -
23.9 MB
53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt -
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54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 -
104.8 MB
54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt -
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55. Zipping and Downloading Our Food Vision Big App Files.mp4 -
39.8 MB
55. Zipping and Downloading Our Food Vision Big App Files.srt -
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56. Deploying Food Vision Big to Hugging Face Spaces.mp4 -
162.5 MB
56. Deploying Food Vision Big to Hugging Face Spaces.srt -
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57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 -
81.8 MB
57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt -
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6. What We Are Going to Cover.mp4 -
40.8 MB
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7. Getting Setup to Code.mp4 -
62.9 MB
7. Getting Setup to Code.srt -
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8. Downloading a Dataset for Food Vision Mini.mp4 -
39.3 MB
8. Downloading a Dataset for Food Vision Mini.srt -
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9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 -
58.6 MB
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1. Introduction to PyTorch 2.0.mp4 -
82.2 MB
1. Introduction to PyTorch 2.0.srt -
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10. Creating a Function to Setup Our Model and Transforms.mp4 -
99.6 MB
10. Creating a Function to Setup Our Model and Transforms.srt -
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11. Discussing How to Get Better Relative Speedups for Training Models.mp4 -
70.1 MB
11. Discussing How to Get Better Relative Speedups for Training Models.srt -
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12. Setting the Batch Size and Data Size Programmatically.mp4 -
71.0 MB
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10.0 KB
13. Getting More Potential Speedups with TensorFloat-32.mp4 -
83.8 MB
13. Getting More Potential Speedups with TensorFloat-32.srt -
13.6 KB
14. Downloading the CIFAR10 Dataset.mp4 -
67.6 MB
14. Downloading the CIFAR10 Dataset.srt -
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15. Creating Training and Test DataLoaders.mp4 -
67.8 MB
15. Creating Training and Test DataLoaders.srt -
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16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp4 -
60.7 MB
16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.srt -
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17. Experiment 1 - Single Run without torch.compile.mp4 -
78.1 MB
17. Experiment 1 - Single Run without torch.compile.srt -
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18. Experiment 2 - Single Run with torch.compile.mp4 -
105.6 MB
18. Experiment 2 - Single Run with torch.compile.srt -
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19. Comparing the Results of Experiment 1 and 2.mp4 -
120.6 MB
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2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 -
15.1 MB
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2.1 PyTorch 2.0 tutorial on learnpytorch.io.html -
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20. Saving the Results of Experiment 1 and 2.mp4 -
58.0 MB
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21. Preparing Functions for Experiment 3 and 4.mp4 -
116.3 MB
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22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 -
132.8 MB
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23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 -
105.0 MB
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24. Comparing the Results of Experiment 3 and 4.mp4 -
62.8 MB
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25. Potential Extensions and Resources to Learn More.mp4 -
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3. Getting Started with PyTorch 2 in Google Colab.mp4 -
44.6 MB
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3.1 PyTorch 2.0 tutorial on learnpytorch.io.html -
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4. PyTorch 2.0 - 30 Second Intro.mp4 -
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5. Getting Setup for PyTorch 2.mp4 -
27.1 MB
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6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 -
77.5 MB
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7. Setting the Default Device in PyTorch 2.mp4 -
103.0 MB
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8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 -
57.6 MB
8. Discussing the Experiments We Are Going to Run for PyTorch 2.srt -
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9. Introduction to PyTorch 2.mp4 -
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1. Thank You!.mp4 -
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2. Become An Alumni.html -
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3. Endorsements on LinkedIn.html -
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4. Learning Guideline.html -
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1. Why Use Machine Learning or Deep Learning.mp4 -
13.8 MB
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10. How To and How Not To Approach This Course.mp4 -
37.7 MB
10. How To and How Not To Approach This Course.srt -
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11. Important Resources For This Course.mp4 -
58.3 MB
11. Important Resources For This Course.srt -
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12. Getting Setup to Write PyTorch Code.mp4 -
70.0 MB
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13. Introduction to PyTorch Tensors.mp4 -
94.0 MB
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14. Creating Random Tensors in PyTorch.mp4 -
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15. Creating Tensors With Zeros and Ones in PyTorch.mp4 -
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16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 -
32.6 MB
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17. Dealing With Tensor Data Types.mp4 -
81.4 MB
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18. Getting Tensor Attributes.mp4 -
66.4 MB
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19. Manipulating Tensors (Tensor Operations).mp4 -
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2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 -
35.3 MB
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20. Matrix Multiplication (Part 1).mp4 -
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21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 -
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22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 -
97.3 MB
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23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 -
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24. Finding The Positional Min and Max of Tensors.mp4 -
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24. Finding The Positional Min and Max of Tensors.srt -
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25. Reshaping, Viewing and Stacking Tensors.mp4 -
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26. Squeezing, Unsqueezing and Permuting Tensors.mp4 -
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27. Selecting Data From Tensors (Indexing).mp4 -
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28. PyTorch Tensors and NumPy.mp4 -
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29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 -
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3. Machine Learning vs. Deep Learning.mp4 -
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30. Different Ways of Accessing a GPU in PyTorch.mp4 -
113.0 MB
30. Different Ways of Accessing a GPU in PyTorch.srt -
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31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 -
64.5 MB
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32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 -
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4. Anatomy of Neural Networks.mp4 -
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5. Different Types of Learning Paradigms.mp4 -
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6. What Can Deep Learning Be Used For.mp4 -
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7. What Is and Why PyTorch.mp4 -
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8. What Are Tensors.mp4 -
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9. What We Are Going To Cover With PyTorch.mp4 -
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1. Introduction and Where You Can Get Help.mp4 -
28.6 MB
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10. Making Predictions With Our Random Model Using Inference Mode.mp4 -
107.0 MB
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11. Training a Model Intuition (The Things We Need).mp4 -
69.5 MB
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12. Setting Up an Optimizer and a Loss Function.mp4 -
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13. PyTorch Training Loop Steps and Intuition.mp4 -
128.8 MB
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14. Writing Code for a PyTorch Training Loop.mp4 -
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15. Reviewing the Steps in a Training Loop Step by Step.mp4 -
177.4 MB
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16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 -
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17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 -
135.0 MB
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18. Reviewing What Happens in a Testing Loop Step by Step.mp4 -
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19. Writing Code to Save a PyTorch Model.mp4 -
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2. Getting Setup and What We Are Covering.mp4 -
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20. Writing Code to Load a PyTorch Model.mp4 -
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21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 -
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22. Putting Everything Together (Part 1) Data.mp4 -
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23. Putting Everything Together (Part 2) Building a Model.mp4 -
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24. Putting Everything Together (Part 3) Training a Model.mp4 -
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25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 -
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26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 -
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27. Exercise Imposter Syndrome.mp4 -
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28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 -
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3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 -
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4. Splitting Our Data Into Training and Test Sets.mp4 -
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5. Building a function to Visualize Our Data.mp4 -
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6. Creating Our First PyTorch Model for Linear Regression.mp4 -
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7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 -
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8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 -
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9. Checking Out the Internals of Our PyTorch Model.mp4 -
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1. Introduction to Machine Learning Classification With PyTorch.mp4 -
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10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 -
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11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 -
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12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 -
126.8 MB
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13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 -
150.0 MB
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14. Discussing Options to Improve a Model.mp4 -
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15. Creating a New Model with More Layers and Hidden Units.mp4 -
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16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 -
118.6 MB
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17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 -
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17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt -
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18. Building and Training a Model to Fit on Straight Line Data.mp4 -
71.7 MB
18. Building and Training a Model to Fit on Straight Line Data.srt -
15.7 KB
19. Evaluating Our Models Predictions on Straight Line Data.mp4 -
50.8 MB
19. Evaluating Our Models Predictions on Straight Line Data.srt -
8.6 KB
2. Classification Problem Example Input and Output Shapes.mp4 -
50.0 MB
2. Classification Problem Example Input and Output Shapes.srt -
14.5 KB
20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 -
96.5 MB
20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt -
15.7 KB
21. Building Our First Neural Network with Non-Linearity.mp4 -
92.6 MB
21. Building Our First Neural Network with Non-Linearity.srt -
15.5 KB
22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 -
150.6 MB
22. Writing Training and Testing Code for Our First Non-Linear Model.srt -
22.9 KB
23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 -
53.0 MB
23. Making Predictions with and Evaluating Our First Non-Linear Model.srt -
8.7 KB
24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 -
80.7 MB
24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt -
14.7 KB
25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 -
97.4 MB
25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt -
17.9 KB
26. Creating a Multi-Class Classification Model with PyTorch.mp4 -
107.4 MB
26. Creating a Multi-Class Classification Model with PyTorch.srt -
18.3 KB
27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 -
65.1 MB
27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt -
10.1 KB
28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 -
97.0 MB
28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt -
16.7 KB
29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 -
150.1 MB
29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt -
25.0 KB
3. Typical Architecture of a Classification Neural Network (Overview).mp4 -
67.0 MB
3. Typical Architecture of a Classification Neural Network (Overview).srt -
10.0 KB
30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 -
77.0 MB
30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt -
13.2 KB
31. Discussing a Few More Classification Metrics.mp4 -
97.5 MB
31. Discussing a Few More Classification Metrics.srt -
13.7 KB
32. PyTorch Classification Exercises and Extra-Curriculum.mp4 -
41.5 MB
32. PyTorch Classification Exercises and Extra-Curriculum.srt -
4.4 KB
4. Making a Toy Classification Dataset.mp4 -
91.5 MB
4. Making a Toy Classification Dataset.srt -
18.0 KB
5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 -
81.1 MB
5. Turning Our Data into Tensors and Making a Training and Test Split.srt -
17.8 KB
6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 -
31.9 MB
6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt -
6.5 KB
7. Coding a Small Neural Network to Handle Our Classification Data.mp4 -
86.8 MB
7. Coding a Small Neural Network to Handle Our Classification Data.srt -
15.8 KB
8. Making Our Neural Network Visual.mp4 -
91.3 MB
8. Making Our Neural Network Visual.srt -
11.0 KB
9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 -
123.2 MB
9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt -
20.7 KB
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1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 -
113.7 MB
1. What Is a Computer Vision Problem and What We Are Going to Cover.srt -
20.3 KB
10. Creating a Loss Function an Optimizer for Model 0.mp4 -
110.5 MB
10. Creating a Loss Function an Optimizer for Model 0.srt -
15.3 KB
11. Creating a Function to Time Our Modelling Code.mp4 -
45.6 MB
11. Creating a Function to Time Our Modelling Code.srt -
8.1 KB
12. Writing Training and Testing Loops for Our Batched Data.mp4 -
157.6 MB
12. Writing Training and Testing Loops for Our Batched Data.srt -
31.2 KB
13. Writing an Evaluation Function to Get Our Models Results.mp4 -
106.8 MB
13. Writing an Evaluation Function to Get Our Models Results.srt -
20.1 KB
14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 -
44.3 MB
14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt -
6.1 KB
15. Model 1 Creating a Model with Non-Linear Functions.mp4 -
86.4 MB
15. Model 1 Creating a Model with Non-Linear Functions.srt -
13.5 KB
16. Mode 1 Creating a Loss Function and Optimizer.mp4 -
31.3 MB
16. Mode 1 Creating a Loss Function and Optimizer.srt -
4.6 KB
17. Turing Our Training Loop into a Function.mp4 -
70.9 MB
17. Turing Our Training Loop into a Function.srt -
12.1 KB
18. Turing Our Testing Loop into a Function.mp4 -
50.9 MB
18. Turing Our Testing Loop into a Function.srt -
9.6 KB
19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 -
108.4 MB
19. Training and Testing Model 1 with Our Training and Testing Functions.srt -
17.9 KB
2. Computer Vision Input and Output Shapes.mp4 -
85.0 MB
2. Computer Vision Input and Output Shapes.srt -
16.5 KB
20. Getting a Results Dictionary for Model 1.mp4 -
41.3 MB
20. Getting a Results Dictionary for Model 1.srt -
6.1 KB
21. Model 2 Convolutional Neural Networks High Level Overview.mp4 -
94.6 MB
21. Model 2 Convolutional Neural Networks High Level Overview.srt -
13.3 KB
22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 -
208.3 MB
22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt -
30.9 KB
23. Model 2 Breaking Down Conv2D Step by Step.mp4 -
162.7 MB
23. Model 2 Breaking Down Conv2D Step by Step.srt -
23.6 KB
24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 -
158.1 MB
24. Model 2 Breaking Down MaxPool2D Step by Step.srt -
22.7 KB
25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 -
174.8 MB
25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt -
20.1 KB
26. Model 2 Setting Up a Loss Function and Optimizer.mp4 -
27.9 MB
26. Model 2 Setting Up a Loss Function and Optimizer.srt -
3.6 KB
27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 -
76.8 MB
27. Model 2 Training Our First CNN and Evaluating Its Results.srt -
11.8 KB
28. Comparing the Results of Our Modelling Experiments.mp4 -
61.8 MB
28. Comparing the Results of Our Modelling Experiments.srt -
11.0 KB
29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 -
83.7 MB
29. Making Predictions on Random Test Samples with the Best Trained Model.srt -
16.2 KB
3. What Is a Convolutional Neural Network (CNN).mp4 -
55.4 MB
3. What Is a Convolutional Neural Network (CNN).srt -
8.1 KB
30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 -
63.5 MB
30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt -
12.2 KB
31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 -
160.8 MB
31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.srt -
21.6 KB
32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 -
67.0 MB
32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt -
10.0 KB
33. Saving and Loading Our Best Performing Model.mp4 -
98.1 MB
33. Saving and Loading Our Best Performing Model.srt -
17.1 KB
34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 -
81.9 MB
34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt -
9.4 KB
4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 -
89.2 MB
4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt -
14.7 KB
5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 -
154.0 MB
5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt -
23.8 KB
6. Visualizing Random Samples of Data.mp4 -
68.1 MB
6. Visualizing Random Samples of Data.srt -
15.5 KB
7. DataLoader Overview Understanding Mini-Batches.mp4 -
60.2 MB
7. DataLoader Overview Understanding Mini-Batches.srt -
10.4 KB
8. Turning Our Datasets Into DataLoaders.mp4 -
100.2 MB
8. Turning Our Datasets Into DataLoaders.srt -
19.4 KB
9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 -
136.9 MB
9. Model 0 Creating a Baseline Model with Two Linear Layers.srt -
21.7 KB
1. What Is a Custom Dataset and What We Are Going to Cover.mp4 -
92.6 MB
1. What Is a Custom Dataset and What We Are Going to Cover.srt -
15.0 KB
10. Visualizing a Loaded Image From the Train Dataset.mp4 -
76.7 MB
10. Visualizing a Loaded Image From the Train Dataset.srt -
10.3 KB
11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 -
84.3 MB
11. Turning Our Image Datasets into PyTorch Dataloaders.srt -
12.3 KB
12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 -
74.7 MB
12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt -
10.4 KB
13. Creating a Helper Function to Get Class Names From a Directory.mp4 -
79.1 MB
13. Creating a Helper Function to Get Class Names From a Directory.srt -
11.9 KB
14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 -
176.3 MB
14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt -
22.9 KB
15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 -
69.5 MB
15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.srt -
9.8 KB
16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 -
131.2 MB
16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt -
19.3 KB
17. Turning Our Custom Datasets Into DataLoaders.mp4 -
80.6 MB
17. Turning Our Custom Datasets Into DataLoaders.srt -
9.7 KB
18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 -
166.4 MB
18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt -
20.7 KB
19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 -
77.9 MB
19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt -
11.6 KB
2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 -
49.0 MB
2. Importing PyTorch and Setting Up Device Agnostic Code.srt -
7.8 KB
20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 -
117.2 MB
20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt -
15.6 KB
21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 -
96.5 MB
21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.srt -
12.0 KB
22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 -
65.0 MB
22. Using the Torchinfo Package to Get a Summary of Our Model.srt -
9.5 KB
23. Creating Training and Testing loop Functions.mp4 -
106.2 MB
23. Creating Training and Testing loop Functions.srt -
17.5 KB
24. Creating a Train Function to Train and Evaluate Our Models.mp4 -
103.5 MB
24. Creating a Train Function to Train and Evaluate Our Models.srt -
15.6 KB
25. Training and Evaluating Model 0 With Our Training Functions.mp4 -
89.3 MB
25. Training and Evaluating Model 0 With Our Training Functions.srt -
14.7 KB
26. Plotting the Loss Curves of Model 0.mp4 -
89.4 MB
26. Plotting the Loss Curves of Model 0.srt -
12.5 KB
27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 -
131.8 MB
27. The Balance Between Overfitting and Underfitting and How to Deal With Each.srt -
21.6 KB
28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 -
98.8 MB
28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt -
15.1 KB
29. Constructing and Training Model 1.mp4 -
60.6 MB
29. Constructing and Training Model 1.srt -
9.5 KB
3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 -
151.0 MB
3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt -
19.1 KB
30. Plotting the Loss Curves of Model 1.mp4 -
31.7 MB
30. Plotting the Loss Curves of Model 1.srt -
5.1 KB
31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 -
89.3 MB
31. Plotting the Loss Curves of All of Our Models Against Each Other.srt -
15.8 KB
32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 -
51.7 MB
32. Predicting on Custom Data (Part 1) Downloading an Image.srt -
7.7 KB
33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 -
68.0 MB
33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.srt -
10.7 KB
34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 -
127.0 MB
34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.srt -
19.7 KB
35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 -
36.1 MB
35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.srt -
5.9 KB
36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 -
113.0 MB
36. Predicting on Custom Data (Part 5) Putting It All Together.srt -
18.4 KB
37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 -
73.3 MB
37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt -
9.3 KB
4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 -
87.6 MB
4. Becoming One With the Data (Part 1) Exploring the Data Format.srt -
12.1 KB
5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 -
115.3 MB
5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt -
17.2 KB
6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 -
51.9 MB
6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt -
7.1 KB
7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 -
81.7 MB
7. Transforming Data (Part 1) Turning Images Into Tensors.srt -
11.7 KB
8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 -
127.6 MB
8. Transforming Data (Part 2) Visualizing Transformed Images.srt -
16.7 KB
9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 -
98.2 MB
9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt -
13.3 KB
[CourseClub.Me].url -
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1. What Is Going Modular and What We Are Going to Cover.mp4 -
100.1 MB
1. What Is Going Modular and What We Are Going to Cover.srt -
18.0 KB
10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 -
80.7 MB
10. Going Modular Summary, Exercises and Extra-Curriculum.srt -
8.9 KB
2. Going Modular Notebook (Part 1) Running It End to End.mp4 -
104.9 MB
2. Going Modular Notebook (Part 1) Running It End to End.srt -
11.5 KB
3. Downloading a Dataset.mp4 -
67.6 MB
3. Downloading a Dataset.srt -
7.2 KB
4. Writing the Outline for Our First Python Script to Setup the Data.mp4 -
156.8 MB
4. Writing the Outline for Our First Python Script to Setup the Data.srt -
18.9 KB
5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 -
135.1 MB
5. Creating a Python Script to Create Our PyTorch DataLoaders.srt -
15.9 KB
6. Turning Our Model Building Code into a Python Script.mp4 -
115.1 MB
6. Turning Our Model Building Code into a Python Script.srt -
13.4 KB
7. Turning Our Model Training Code into a Python Script.mp4 -
80.0 MB
7. Turning Our Model Training Code into a Python Script.srt -
8.6 KB
8. Turning Our Utility Function to Save a Model into a Python Script.mp4 -
75.8 MB
8. Turning Our Utility Function to Save a Model into a Python Script.srt -
9.0 KB
9. Creating a Training Script to Train Our Model in One Line of Code.mp4 -
165.5 MB
9. Creating a Training Script to Train Our Model in One Line of Code.srt -
21.9 KB
1. Introduction What is Transfer Learning and Why Use It.mp4 -
97.3 MB
1. Introduction What is Transfer Learning and Why Use It.srt -
15.7 KB
10. Different Kinds of Transfer Learning.mp4 -
57.0 MB
10. Different Kinds of Transfer Learning.srt -
10.7 KB
11. Getting a Summary of the Different Layers of Our Model.mp4 -
76.0 MB
11. Getting a Summary of the Different Layers of Our Model.srt -
10.0 KB
12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 -
160.7 MB
12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt -
19.9 KB
13. Training Our First Transfer Learning Feature Extractor Model.mp4 -
74.8 MB
13. Training Our First Transfer Learning Feature Extractor Model.srt -
11.6 KB
14. Plotting the Loss curves of Our Transfer Learning Model.mp4 -
58.9 MB
14. Plotting the Loss curves of Our Transfer Learning Model.srt -
9.4 KB
15. Outlining the Steps to Make Predictions on the Test Images.mp4 -
66.7 MB
15. Outlining the Steps to Make Predictions on the Test Images.srt -
10.5 KB
16. Creating a Function Predict On and Plot Images.mp4 -
101.7 MB
16. Creating a Function Predict On and Plot Images.srt -
14.2 KB
17. Making and Plotting Predictions on Test Images.mp4 -
78.1 MB
17. Making and Plotting Predictions on Test Images.srt -
10.7 KB
18. Making a Prediction on a Custom Image.mp4 -
67.8 MB
18. Making a Prediction on a Custom Image.srt -
9.4 KB
19. Main Takeaways, Exercises and Extra- Curriculum.mp4 -
44.4 MB
19. Main Takeaways, Exercises and Extra- Curriculum.srt -
5.2 KB
2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 -
55.9 MB
2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt -
8.3 KB
3. Installing the Latest Versions of Torch and Torchvision.mp4 -
82.4 MB
3. Installing the Latest Versions of Torch and Torchvision.srt -
11.1 KB
4. Downloading Our Previously Written Code from Going Modular.mp4 -
83.7 MB
4. Downloading Our Previously Written Code from Going Modular.srt -
10.3 KB
5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 -
72.2 MB
5. Downloading Pizza, Steak, Sushi Image Data from Github.srt -
11.2 KB
6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 -
141.5 MB
6. Turning Our Data into DataLoaders with Manually Created Transforms.srt -
19.4 KB
7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 -
139.7 MB
7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt -
18.4 KB
8. Which Pretrained Model Should You Use.mp4 -
128.8 MB
8. Which Pretrained Model Should You Use.srt -
17.7 KB
9. Setting Up a Pretrained Model with Torchvision.mp4 -
113.1 MB
9. Setting Up a Pretrained Model with Torchvision.srt -
16.6 KB
[CourseClub.Me].url -
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1. What Is Experiment Tracking and Why Track Experiments.mp4 -
61.9 MB
1. What Is Experiment Tracking and Why Track Experiments.srt -
11.3 KB
10. Creating a Function to Create SummaryWriter Instances.mp4 -
80.1 MB
10. Creating a Function to Create SummaryWriter Instances.srt -
14.2 KB
11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 -
66.5 MB
11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt -
6.6 KB
12. What Experiments Should You Try.mp4 -
46.9 MB
12. What Experiments Should You Try.srt -
8.5 KB
13. Discussing the Experiments We Are Going to Try.mp4 -
48.3 MB
13. Discussing the Experiments We Are Going to Try.srt -
8.1 KB
14. Downloading Datasets for Our Modelling Experiments.mp4 -
66.4 MB
14. Downloading Datasets for Our Modelling Experiments.srt -
8.9 KB
15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 -
78.1 MB
15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt -
11.3 KB
16. Creating Functions to Prepare Our Feature Extractor Models.mp4 -
159.2 MB
16. Creating Functions to Prepare Our Feature Extractor Models.srt -
22.7 KB
17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 -
127.6 MB
17. Coding Out the Steps to Run a Series of Modelling Experiments.srt -
19.6 KB
18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 -
45.7 MB
18. Running Eight Different Modelling Experiments in 5 Minutes.srt -
6.3 KB
19. Viewing Our Modelling Experiments in TensorBoard.mp4 -
140.3 MB
19. Viewing Our Modelling Experiments in TensorBoard.srt -
19.7 KB
2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 -
93.4 MB
2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt -
12.4 KB
20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 -
99.2 MB
20. Loading the Best Model and Making Predictions on Random Images from the Test Set.srt -
14.8 KB
21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 -
39.7 MB
21. Making a Prediction on Our Own Custom Image with the Best Model.srt -
5.8 KB
22. Main Takeaways, Exercises and Extra- Curriculum.mp4 -
43.6 MB
22. Main Takeaways, Exercises and Extra- Curriculum.srt -
6.6 KB
3. Creating a Function to Download Data.mp4 -
95.2 MB
3. Creating a Function to Download Data.srt -
14.6 KB
4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 -
92.7 MB
4. Turning Our Data into DataLoaders Using Manual Transforms.srt -
12.3 KB
5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 -
82.0 MB
5. Turning Our Data into DataLoaders Using Automatic Transforms.srt -
11.1 KB
6. Preparing a Pretrained Model for Our Own Problem.mp4 -
113.2 MB
6. Preparing a Pretrained Model for Our Own Problem.srt -
15.7 KB
7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 -
150.3 MB
7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.srt -
20.0 KB
8. Training a Single Model and Saving the Results to TensorBoard.mp4 -
41.8 MB
8. Training a Single Model and Saving the Results to TensorBoard.srt -
6.7 KB
9. Exploring Our Single Models Results with TensorBoard.mp4 -
116.3 MB
9. Exploring Our Single Models Results with TensorBoard.srt -
16.7 KB
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