PyTorch Deep Learning and Artificial Intelligence updated
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| Torrent Added : | at Oct. 25, 2023, 11:32 p.m. in Other |
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PyTorch Deep Learning and Artificial Intelligence updated
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PyTorch Deep Learning and Artificial Intelligence updated
2. Windows-Focused Environment Setup 2018.mp4 -
READ_ME.txt -
1. Welcome.mp4 -
1. Welcome.srt -
2. Overview and Outline.mp4 -
2. Overview and Outline.srt -
3. Where to get the Code.mp4 -
3. Where to get the Code.srt -
1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 -
1. Intro to Google Colab, how to use a GPU or TPU for free.srt -
2. Uploading your own data to Google Colab.mp4 -
2. Uploading your own data to Google Colab.srt -
3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 -
3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt -
1. What is Machine Learning.mp4 -
1. What is Machine Learning.srt -
2. Regression Basics.mp4 -
2. Regression Basics.srt -
3. Regression Code Preparation.mp4 -
3. Regression Code Preparation.srt -
4. Regression Notebook.mp4 -
4. Regression Notebook.srt -
5. Moore's Law.mp4 -
5. Moore's Law.srt -
6. Moore's Law Notebook.mp4 -
6. Moore's Law Notebook.srt -
7. Linear Classification Basics.mp4 -
7. Linear Classification Basics.srt -
8. Classification Code Preparation.mp4 -
8. Classification Code Preparation.srt -
9. Classification Notebook.mp4 -
9. Classification Notebook.srt -
10. Saving and Loading a Model.mp4 -
10. Saving and Loading a Model.srt -
11. A Short Neuroscience Primer.mp4 -
11. A Short Neuroscience Primer.srt -
12. How does a model learn.mp4 -
12. How does a model learn.srt -
13. Model With Logits.mp4 -
13. Model With Logits.srt -
14. Train Sets vs. Validation Sets vs. Test Sets.mp4 -
14. Train Sets vs. Validation Sets vs. Test Sets.srt -
1. Artificial Neural Networks Section Introduction.mp4 -
1. Artificial Neural Networks Section Introduction.srt -
2. Forward Propagation.mp4 -
2. Forward Propagation.srt -
3. The Geometrical Picture.mp4 -
3. The Geometrical Picture.srt -
4. Activation Functions.mp4 -
4. Activation Functions.srt -
5. Multiclass Classification.mp4 -
5. Multiclass Classification.srt -
6. How to Represent Images.mp4 -
6. How to Represent Images.srt -
7. Code Preparation (ANN).mp4 -
7. Code Preparation (ANN).srt -
8. ANN for Image Classification.mp4 -
8. ANN for Image Classification.srt -
9. ANN for Regression.mp4 -
9. ANN for Regression.srt -
1. What is Convolution (part 1).mp4 -
1. What is Convolution (part 1).srt -
2. What is Convolution (part 2).mp4 -
2. What is Convolution (part 2).srt -
3. What is Convolution (part 3).mp4 -
3. What is Convolution (part 3).srt -
4. Convolution on Color Images.mp4 -
4. Convolution on Color Images.srt -
5. CNN Architecture.mp4 -
5. CNN Architecture.srt -
6. CNN Code Preparation (part 1).mp4 -
6. CNN Code Preparation (part 1).srt -
7. CNN Code Preparation (part 2).mp4 -
7. CNN Code Preparation (part 2).srt -
8. CNN Code Preparation (part 3).mp4 -
8. CNN Code Preparation (part 3).srt -
9. CNN for Fashion MNIST.mp4 -
9. CNN for Fashion MNIST.srt -
10. CNN for CIFAR-10.mp4 -
10. CNN for CIFAR-10.srt -
11. Data Augmentation.mp4 -
11. Data Augmentation.srt -
12. Batch Normalization.mp4 -
12. Batch Normalization.srt -
13. Improving CIFAR-10 Results.mp4 -
13. Improving CIFAR-10 Results.srt -
1. Sequence Data.mp4 -
1. Sequence Data.srt -
2. Forecasting.mp4 -
2. Forecasting.srt -
3. Autoregressive Linear Model for Time Series Prediction.mp4 -
3. Autoregressive Linear Model for Time Series Prediction.srt -
4. Proof that the Linear Model Works.mp4 -
4. Proof that the Linear Model Works.srt -
5. Recurrent Neural Networks.mp4 -
5. Recurrent Neural Networks.srt -
6. RNN Code Preparation.mp4 -
6. RNN Code Preparation.srt -
7. RNN for Time Series Prediction.mp4 -
7. RNN for Time Series Prediction.srt -
8. Paying Attention to Shapes.mp4 -
8. Paying Attention to Shapes.srt -
9. GRU and LSTM (pt 1).mp4 -
9. GRU and LSTM (pt 1).srt -
10. GRU and LSTM (pt 2).mp4 -
10. GRU and LSTM (pt 2).srt -
11. A More Challenging Sequence.mp4 -
11. A More Challenging Sequence.srt -
12. RNN for Image Classification (Theory).mp4 -
12. RNN for Image Classification (Theory).srt -
13. RNN for Image Classification (Code).mp4 -
13. RNN for Image Classification (Code).srt -
14. Stock Return Predictions using LSTMs (pt 1).mp4 -
14. Stock Return Predictions using LSTMs (pt 1).srt -
15. Stock Return Predictions using LSTMs (pt 2).mp4 -
15. Stock Return Predictions using LSTMs (pt 2).srt -
16. Stock Return Predictions using LSTMs (pt 3).mp4 -
16. Stock Return Predictions using LSTMs (pt 3).srt -
17. Other Ways to Forecast.mp4 -
17. Other Ways to Forecast.srt -
1. Embeddings.mp4 -
1. Embeddings.srt -
2. Neural Networks with Embeddings.mp4 -
2. Neural Networks with Embeddings.srt -
3. Text Preprocessing (pt 1).mp4 -
3. Text Preprocessing (pt 1).srt -
4. Text Preprocessing (pt 2).mp4 -
4. Text Preprocessing (pt 2).srt -
5. Text Preprocessing (pt 3).mp4 -
5. Text Preprocessing (pt 3).srt -
6. Text Classification with LSTMs.mp4 -
6. Text Classification with LSTMs.srt -
7. CNNs for Text.mp4 -
7. CNNs for Text.srt -
8. Text Classification with CNNs.mp4 -
8. Text Classification with CNNs.srt -
9. VIP Making Predictions with a Trained NLP Model.mp4 -
9. VIP Making Predictions with a Trained NLP Model.srt -
1. Recommender Systems with Deep Learning Theory.mp4 -
1. Recommender Systems with Deep Learning Theory.srt -
2. Recommender Systems with Deep Learning Code Preparation.mp4 -
2. Recommender Systems with Deep Learning Code Preparation.srt -
3. Recommender Systems with Deep Learning Code (pt 1).mp4 -
3. Recommender Systems with Deep Learning Code (pt 1).srt -
4. Recommender Systems with Deep Learning Code (pt 2).mp4 -
4. Recommender Systems with Deep Learning Code (pt 2).srt -
5. VIP Making Predictions with a Trained Recommender Model.mp4 -
5. VIP Making Predictions with a Trained Recommender Model.srt -
1. Transfer Learning Theory.mp4 -
1. Transfer Learning Theory.srt -
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 -
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt -
3. Large Datasets.mp4 -
3. Large Datasets.srt -
4. 2 Approaches to Transfer Learning.mp4 -
4. 2 Approaches to Transfer Learning.srt -
5. Transfer Learning Code (pt 1).mp4 -
5. Transfer Learning Code (pt 1).srt -
6. Transfer Learning Code (pt 2).mp4 -
6. Transfer Learning Code (pt 2).srt -
1. GAN Theory.mp4 -
1. GAN Theory.srt -
2. GAN Code Preparation.mp4 -
2. GAN Code Preparation.srt -
3. GAN Code.mp4 -
3. GAN Code.srt -
1. Deep Reinforcement Learning Section Introduction.mp4 -
1. Deep Reinforcement Learning Section Introduction.srt -
2. Elements of a Reinforcement Learning Problem.mp4 -
2. Elements of a Reinforcement Learning Problem.srt -
3. States, Actions, Rewards, Policies.mp4 -
3. States, Actions, Rewards, Policies.srt -
4. Markov Decision Processes (MDPs).mp4 -
4. Markov Decision Processes (MDPs).srt -
5. The Return.mp4 -
5. The Return.srt -
6. Value Functions and the Bellman Equation.mp4 -
6. Value Functions and the Bellman Equation.srt -
7. What does it mean to “learn”.mp4 -
7. What does it mean to “learn”.srt -
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 -
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt -
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 -
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt -
10. Epsilon-Greedy.mp4 -
10. Epsilon-Greedy.srt -
11. Q-Learning.mp4 -
11. Q-Learning.srt -
12. Deep Q-Learning DQN (pt 1).mp4 -
12. Deep Q-Learning DQN (pt 1).srt -
13. Deep Q-Learning DQN (pt 2).mp4 -
13. Deep Q-Learning DQN (pt 2).srt -
14. How to Learn Reinforcement Learning.mp4 -
14. How to Learn Reinforcement Learning.srt -
1. Reinforcement Learning Stock Trader Introduction.mp4 -
1. Reinforcement Learning Stock Trader Introduction.srt -
2. Data and Environment.mp4 -
2. Data and Environment.srt -
3. Replay Buffer.mp4 -
3. Replay Buffer.srt -
4. Program Design and Layout.mp4 -
4. Program Design and Layout.srt -
5. Code pt 1.mp4 -
5. Code pt 1.srt -
6. Code pt 2.mp4 -
6. Code pt 2.srt -
7. Code pt 3.mp4 -
7. Code pt 3.srt -
8. Code pt 4.mp4 -
8. Code pt 4.srt -
9. Reinforcement Learning Stock Trader Discussion.mp4 -
9. Reinforcement Learning Stock Trader Discussion.srt -
1. Custom Loss and Estimating Prediction Uncertainty.mp4 -
1. Custom Loss and Estimating Prediction Uncertainty.srt -
2. Estimating Prediction Uncertainty Code.mp4 -
2. Estimating Prediction Uncertainty Code.srt -
1. Facial Recognition Section Introduction.mp4 -
1. Facial Recognition Section Introduction.srt -
2. Siamese Networks.mp4 -
2. Siamese Networks.srt -
3. Code Outline.mp4 -
3. Code Outline.srt -
4. Loading in the data.mp4 -
4. Loading in the data.srt -
5. Splitting the data into train and test.mp4 -
5. Splitting the data into train and test.srt -
6. Converting the data into pairs.mp4 -
6. Converting the data into pairs.srt -
7. Generating Generators.mp4 -
7. Generating Generators.srt -
8. Creating the model and loss.mp4 -
8. Creating the model and loss.srt -
9. Accuracy and imbalanced classes.mp4 -
9. Accuracy and imbalanced classes.srt -
10. Facial Recognition Section Summary.mp4 -
10. Facial Recognition Section Summary.srt -
1. Mean Squared Error.mp4 -
1. Mean Squared Error.srt -
2. Binary Cross Entropy.mp4 -
2. Binary Cross Entropy.srt -
3. Categorical Cross Entropy.mp4 -
3. Categorical Cross Entropy.srt -
1. Gradient Descent.mp4 -
1. Gradient Descent.srt -
2. Stochastic Gradient Descent.mp4 -
2. Stochastic Gradient Descent.srt -
3. Momentum.mp4 -
3. Momentum.srt -
4. Variable and Adaptive Learning Rates.mp4 -
4. Variable and Adaptive Learning Rates.srt -
5. Adam.mp4 -
5. Adam.srt -
1. Links To Colab Notebooks.html -
2. Links to VIP Notebooks.html -
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 -
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt -
READ_ME.txt -
2. Windows-Focused Environment Setup 2018.srt -
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 -
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt -
1. What is the Appendix.mp4 -
1. What is the Appendix.srt -
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 -
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt -
3. How to Code Yourself (part 1).mp4 -
4. How to Code Yourself (part 2).mp4 -
4. How to Code Yourself (part 2).srt -
5. Proof that using Jupyter Notebook is the same as not using it.mp4 -
5. Proof that using Jupyter Notebook is the same as not using it.srt -
6. How to Succeed in this Course (Long Version).mp4 -
6. How to Succeed in this Course (Long Version).srt -
7. What order should I take your courses in (part 1).mp4 -
7. What order should I take your courses in (part 1).srt -
8. What order should I take your courses in (part 2).mp4 -
8. What order should I take your courses in (part 2).srt -
9. BONUS Where to get discount coupons and FREE deep learning material.mp4 -
9. BONUS Where to get discount coupons and FREE deep learning material.srt -
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2. Windows-Focused Environment Setup 2018.mp4 -
180.7 MB
READ_ME.txt -
404 bytes
1. Welcome.mp4 -
35.7 MB
1. Welcome.srt -
5.7 KB
2. Overview and Outline.mp4 -
79.7 MB
2. Overview and Outline.srt -
17.7 KB
3. Where to get the Code.mp4 -
30.2 MB
3. Where to get the Code.srt -
7.6 KB
1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 -
60.5 MB
1. Intro to Google Colab, how to use a GPU or TPU for free.srt -
14.3 KB
2. Uploading your own data to Google Colab.mp4 -
90.5 MB
2. Uploading your own data to Google Colab.srt -
14.5 KB
3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 -
44.4 MB
3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt -
12.1 KB
1. What is Machine Learning.mp4 -
70.6 MB
1. What is Machine Learning.srt -
18.4 KB
2. Regression Basics.mp4 -
73.0 MB
2. Regression Basics.srt -
20.1 KB
3. Regression Code Preparation.mp4 -
45.5 MB
3. Regression Code Preparation.srt -
16.4 KB
4. Regression Notebook.mp4 -
71.9 MB
4. Regression Notebook.srt -
17.5 KB
5. Moore's Law.mp4 -
30.6 MB
5. Moore's Law.srt -
9.1 KB
6. Moore's Law Notebook.mp4 -
78.9 MB
6. Moore's Law Notebook.srt -
15.8 KB
7. Linear Classification Basics.mp4 -
67.2 MB
7. Linear Classification Basics.srt -
19.8 KB
8. Classification Code Preparation.mp4 -
26.5 MB
8. Classification Code Preparation.srt -
9.4 KB
9. Classification Notebook.mp4 -
78.3 MB
9. Classification Notebook.srt -
14.6 KB
10. Saving and Loading a Model.mp4 -
28.8 MB
10. Saving and Loading a Model.srt -
6.6 KB
11. A Short Neuroscience Primer.mp4 -
44.7 MB
11. A Short Neuroscience Primer.srt -
12.3 KB
12. How does a model learn.mp4 -
50.1 MB
12. How does a model learn.srt -
13.8 KB
13. Model With Logits.mp4 -
27.3 MB
13. Model With Logits.srt -
5.3 KB
14. Train Sets vs. Validation Sets vs. Test Sets.mp4 -
52.1 MB
14. Train Sets vs. Validation Sets vs. Test Sets.srt -
14.3 KB
1. Artificial Neural Networks Section Introduction.mp4 -
33.5 MB
1. Artificial Neural Networks Section Introduction.srt -
7.9 KB
2. Forward Propagation.mp4 -
47.1 MB
2. Forward Propagation.srt -
12.2 KB
3. The Geometrical Picture.mp4 -
56.4 MB
3. The Geometrical Picture.srt -
11.5 KB
4. Activation Functions.mp4 -
89.2 MB
4. Activation Functions.srt -
22.6 KB
5. Multiclass Classification.mp4 -
48.7 MB
5. Multiclass Classification.srt -
12.2 KB
6. How to Represent Images.mp4 -
75.4 MB
6. How to Represent Images.srt -
15.3 KB
7. Code Preparation (ANN).mp4 -
67.5 MB
7. Code Preparation (ANN).srt -
19.9 KB
8. ANN for Image Classification.mp4 -
106.3 MB
8. ANN for Image Classification.srt -
22.6 KB
9. ANN for Regression.mp4 -
80.2 MB
9. ANN for Regression.srt -
13.0 KB
1. What is Convolution (part 1).mp4 -
79.7 MB
1. What is Convolution (part 1).srt -
20.1 KB
2. What is Convolution (part 2).mp4 -
24.5 MB
2. What is Convolution (part 2).srt -
7.2 KB
3. What is Convolution (part 3).mp4 -
28.7 MB
3. What is Convolution (part 3).srt -
8.0 KB
4. Convolution on Color Images.mp4 -
76.4 MB
4. Convolution on Color Images.srt -
20.8 KB
5. CNN Architecture.mp4 -
89.5 MB
5. CNN Architecture.srt -
27.8 KB
6. CNN Code Preparation (part 1).mp4 -
76.7 MB
6. CNN Code Preparation (part 1).srt -
22.8 KB
7. CNN Code Preparation (part 2).mp4 -
36.7 MB
7. CNN Code Preparation (part 2).srt -
10.4 KB
8. CNN Code Preparation (part 3).mp4 -
33.7 MB
8. CNN Code Preparation (part 3).srt -
7.2 KB
9. CNN for Fashion MNIST.mp4 -
74.5 MB
9. CNN for Fashion MNIST.srt -
13.5 KB
10. CNN for CIFAR-10.mp4 -
56.7 MB
10. CNN for CIFAR-10.srt -
9.3 KB
11. Data Augmentation.mp4 -
44.5 MB
11. Data Augmentation.srt -
12.5 KB
12. Batch Normalization.mp4 -
23.4 MB
12. Batch Normalization.srt -
6.6 KB
13. Improving CIFAR-10 Results.mp4 -
77.4 MB
13. Improving CIFAR-10 Results.srt -
12.8 KB
1. Sequence Data.mp4 -
114.3 MB
1. Sequence Data.srt -
29.6 KB
2. Forecasting.mp4 -
48.7 MB
2. Forecasting.srt -
13.2 KB
3. Autoregressive Linear Model for Time Series Prediction.mp4 -
81.2 MB
3. Autoregressive Linear Model for Time Series Prediction.srt -
14.7 KB
4. Proof that the Linear Model Works.mp4 -
17.9 MB
4. Proof that the Linear Model Works.srt -
4.6 KB
5. Recurrent Neural Networks.mp4 -
92.6 MB
5. Recurrent Neural Networks.srt -
25.7 KB
6. RNN Code Preparation.mp4 -
55.3 MB
6. RNN Code Preparation.srt -
17.6 KB
7. RNN for Time Series Prediction.mp4 -
71.9 MB
7. RNN for Time Series Prediction.srt -
9.9 KB
8. Paying Attention to Shapes.mp4 -
56.4 MB
8. Paying Attention to Shapes.srt -
11.0 KB
9. GRU and LSTM (pt 1).mp4 -
76.1 MB
9. GRU and LSTM (pt 1).srt -
21.1 KB
10. GRU and LSTM (pt 2).mp4 -
50.6 MB
10. GRU and LSTM (pt 2).srt -
15.0 KB
11. A More Challenging Sequence.mp4 -
86.7 MB
11. A More Challenging Sequence.srt -
10.7 KB
12. RNN for Image Classification (Theory).mp4 -
32.3 MB
12. RNN for Image Classification (Theory).srt -
6.0 KB
13. RNN for Image Classification (Code).mp4 -
20.5 MB
13. RNN for Image Classification (Code).srt -
3.3 KB
14. Stock Return Predictions using LSTMs (pt 1).mp4 -
77.8 MB
14. Stock Return Predictions using LSTMs (pt 1).srt -
16.0 KB
15. Stock Return Predictions using LSTMs (pt 2).mp4 -
43.2 MB
15. Stock Return Predictions using LSTMs (pt 2).srt -
6.8 KB
16. Stock Return Predictions using LSTMs (pt 3).mp4 -
71.1 MB
16. Stock Return Predictions using LSTMs (pt 3).srt -
14.4 KB
17. Other Ways to Forecast.mp4 -
28.3 MB
17. Other Ways to Forecast.srt -
7.2 KB
1. Embeddings.mp4 -
60.0 MB
1. Embeddings.srt -
16.1 KB
2. Neural Networks with Embeddings.mp4 -
15.6 MB
2. Neural Networks with Embeddings.srt -
4.5 KB
3. Text Preprocessing (pt 1).mp4 -
52.3 MB
3. Text Preprocessing (pt 1).srt -
17.9 KB
4. Text Preprocessing (pt 2).mp4 -
44.4 MB
4. Text Preprocessing (pt 2).srt -
15.3 KB
5. Text Preprocessing (pt 3).mp4 -
47.7 MB
5. Text Preprocessing (pt 3).srt -
9.4 KB
6. Text Classification with LSTMs.mp4 -
65.0 MB
6. Text Classification with LSTMs.srt -
10.3 KB
7. CNNs for Text.mp4 -
58.7 MB
7. CNNs for Text.srt -
14.9 KB
8. Text Classification with CNNs.mp4 -
39.3 MB
8. Text Classification with CNNs.srt -
5.6 KB
9. VIP Making Predictions with a Trained NLP Model.mp4 -
48.8 MB
9. VIP Making Predictions with a Trained NLP Model.srt -
9.1 KB
1. Recommender Systems with Deep Learning Theory.mp4 -
64.8 MB
1. Recommender Systems with Deep Learning Theory.srt -
13.7 KB
2. Recommender Systems with Deep Learning Code Preparation.mp4 -
40.1 MB
2. Recommender Systems with Deep Learning Code Preparation.srt -
12.7 KB
3. Recommender Systems with Deep Learning Code (pt 1).mp4 -
69.6 MB
3. Recommender Systems with Deep Learning Code (pt 1).srt -
10.9 KB
4. Recommender Systems with Deep Learning Code (pt 2).mp4 -
76.9 MB
4. Recommender Systems with Deep Learning Code (pt 2).srt -
17.4 KB
5. VIP Making Predictions with a Trained Recommender Model.mp4 -
32.7 MB
5. VIP Making Predictions with a Trained Recommender Model.srt -
6.0 KB
1. Transfer Learning Theory.mp4 -
58.2 MB
1. Transfer Learning Theory.srt -
10.7 KB
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 -
21.7 MB
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt -
5.2 KB
3. Large Datasets.mp4 -
41.3 MB
3. Large Datasets.srt -
9.1 KB
4. 2 Approaches to Transfer Learning.mp4 -
21.8 MB
4. 2 Approaches to Transfer Learning.srt -
6.0 KB
5. Transfer Learning Code (pt 1).mp4 -
77.8 MB
5. Transfer Learning Code (pt 1).srt -
11.6 KB
6. Transfer Learning Code (pt 2).mp4 -
56.3 MB
6. Transfer Learning Code (pt 2).srt -
8.8 KB
1. GAN Theory.mp4 -
92.1 MB
1. GAN Theory.srt -
21.1 KB
2. GAN Code Preparation.mp4 -
28.1 MB
2. GAN Code Preparation.srt -
8.5 KB
3. GAN Code.mp4 -
61.4 MB
3. GAN Code.srt -
10.7 KB
1. Deep Reinforcement Learning Section Introduction.mp4 -
40.7 MB
1. Deep Reinforcement Learning Section Introduction.srt -
8.6 KB
2. Elements of a Reinforcement Learning Problem.mp4 -
104.9 MB
2. Elements of a Reinforcement Learning Problem.srt -
26.2 KB
3. States, Actions, Rewards, Policies.mp4 -
44.1 MB
3. States, Actions, Rewards, Policies.srt -
11.3 KB
4. Markov Decision Processes (MDPs).mp4 -
50.5 MB
4. Markov Decision Processes (MDPs).srt -
12.7 KB
5. The Return.mp4 -
23.4 MB
5. The Return.srt -
6.3 KB
6. Value Functions and the Bellman Equation.mp4 -
47.7 MB
6. Value Functions and the Bellman Equation.srt -
12.5 KB
7. What does it mean to “learn”.mp4 -
32.5 MB
7. What does it mean to “learn”.srt -
8.9 KB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 -
42.6 MB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt -
12.7 KB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 -
57.0 MB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt -
14.9 KB
10. Epsilon-Greedy.mp4 -
41.5 MB
10. Epsilon-Greedy.srt -
7.4 KB
11. Q-Learning.mp4 -
66.8 MB
11. Q-Learning.srt -
17.9 KB
12. Deep Q-Learning DQN (pt 1).mp4 -
60.2 MB
12. Deep Q-Learning DQN (pt 1).srt -
16.4 KB
13. Deep Q-Learning DQN (pt 2).mp4 -
52.2 MB
13. Deep Q-Learning DQN (pt 2).srt -
13.2 KB
14. How to Learn Reinforcement Learning.mp4 -
40.3 MB
14. How to Learn Reinforcement Learning.srt -
7.6 KB
1. Reinforcement Learning Stock Trader Introduction.mp4 -
28.8 MB
1. Reinforcement Learning Stock Trader Introduction.srt -
6.8 KB
2. Data and Environment.mp4 -
55.7 MB
2. Data and Environment.srt -
15.7 KB
3. Replay Buffer.mp4 -
25.0 MB
3. Replay Buffer.srt -
6.9 KB
4. Program Design and Layout.mp4 -
26.9 MB
4. Program Design and Layout.srt -
8.6 KB
5. Code pt 1.mp4 -
66.3 MB
5. Code pt 1.srt -
12.1 KB
6. Code pt 2.mp4 -
70.0 MB
6. Code pt 2.srt -
11.8 KB
7. Code pt 3.mp4 -
58.6 MB
7. Code pt 3.srt -
8.4 KB
8. Code pt 4.mp4 -
52.3 MB
8. Code pt 4.srt -
8.2 KB
9. Reinforcement Learning Stock Trader Discussion.mp4 -
17.2 MB
9. Reinforcement Learning Stock Trader Discussion.srt -
4.4 KB
1. Custom Loss and Estimating Prediction Uncertainty.mp4 -
43.6 MB
1. Custom Loss and Estimating Prediction Uncertainty.srt -
12.8 KB
2. Estimating Prediction Uncertainty Code.mp4 -
42.7 MB
2. Estimating Prediction Uncertainty Code.srt -
8.8 KB
1. Facial Recognition Section Introduction.mp4 -
24.3 MB
1. Facial Recognition Section Introduction.srt -
4.6 KB
2. Siamese Networks.mp4 -
50.5 MB
2. Siamese Networks.srt -
12.8 KB
3. Code Outline.mp4 -
23.9 MB
3. Code Outline.srt -
5.8 KB
4. Loading in the data.mp4 -
35.1 MB
4. Loading in the data.srt -
6.9 KB
5. Splitting the data into train and test.mp4 -
26.3 MB
5. Splitting the data into train and test.srt -
5.1 KB
6. Converting the data into pairs.mp4 -
30.4 MB
6. Converting the data into pairs.srt -
5.8 KB
7. Generating Generators.mp4 -
32.4 MB
7. Generating Generators.srt -
5.7 KB
8. Creating the model and loss.mp4 -
29.4 MB
8. Creating the model and loss.srt -
5.4 KB
9. Accuracy and imbalanced classes.mp4 -
51.1 MB
9. Accuracy and imbalanced classes.srt -
9.5 KB
10. Facial Recognition Section Summary.mp4 -
18.3 MB
10. Facial Recognition Section Summary.srt -
4.4 KB
1. Mean Squared Error.mp4 -
33.8 MB
1. Mean Squared Error.srt -
11.2 KB
2. Binary Cross Entropy.mp4 -
23.7 MB
2. Binary Cross Entropy.srt -
7.3 KB
3. Categorical Cross Entropy.mp4 -
31.7 MB
3. Categorical Cross Entropy.srt -
9.6 KB
1. Gradient Descent.mp4 -
34.9 MB
1. Gradient Descent.srt -
9.8 KB
2. Stochastic Gradient Descent.mp4 -
23.0 MB
2. Stochastic Gradient Descent.srt -
5.4 KB
3. Momentum.mp4 -
34.2 MB
3. Momentum.srt -
7.8 KB
4. Variable and Adaptive Learning Rates.mp4 -
34.9 MB
4. Variable and Adaptive Learning Rates.srt -
15.2 KB
5. Adam.mp4 -
38.9 MB
5. Adam.srt -
13.5 KB
1. Links To Colab Notebooks.html -
7.2 KB
2. Links to VIP Notebooks.html -
256 bytes
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 -
150.7 MB
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt -
14.7 KB
READ_ME.txt -
404 bytes
2. Windows-Focused Environment Setup 2018.srt -
20.0 KB
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 -
167.3 MB
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt -
32.0 KB
1. What is the Appendix.mp4 -
16.4 MB
1. What is the Appendix.srt -
3.7 KB
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 -
105.7 MB
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt -
31.6 KB
3. How to Code Yourself (part 1).mp4 -
71.9 MB
4. How to Code Yourself (part 2).mp4 -
49.2 MB
4. How to Code Yourself (part 2).srt -
13.0 KB
5. Proof that using Jupyter Notebook is the same as not using it.mp4 -
69.5 MB
5. Proof that using Jupyter Notebook is the same as not using it.srt -
14.2 KB
6. How to Succeed in this Course (Long Version).mp4 -
35.2 MB
6. How to Succeed in this Course (Long Version).srt -
14.6 KB
7. What order should I take your courses in (part 1).mp4 -
79.6 MB
7. What order should I take your courses in (part 1).srt -
16.1 KB
8. What order should I take your courses in (part 2).mp4 -
108.2 MB
8. What order should I take your courses in (part 2).srt -
23.0 KB
9. BONUS Where to get discount coupons and FREE deep learning material.mp4 -
37.8 MB
9. BONUS Where to get discount coupons and FREE deep learning material.srt -
7.9 KB
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