2023 Python for Deep Learning and Artificial Intelligence
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| Torrent Added : | at July 8, 2023, 3:02 p.m. in Other |
| Torrent Size : | 7.0 GB |
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2023 Python for Deep Learning and Artificial Intelligence
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2023 Python for Deep Learning and Artificial Intelligence
14. MobileNet Architecture Explained.mp4 -
TutsNode.net.txt -
[TGx]Downloaded from torrentgalaxy.to .txt -
0 -
3. Convolutional Filters.mp4 -
1 -
10. Data Visualization Part 2.mp4 -
2 -
15. EfficientNet Architecture Explained.mp4 -
3 -
1. Jupyter Notebook Introduction.mp4 -
4 -
5. Padding and Strides.mp4 -
5 -
11. AlexNet Architecture Explained.mp4 -
6 -
17. Online Prediction of Flowers Classes.mp4 -
7 -
6. Data Display in Subplots Matrix.mp4 -
8 -
4. Fashion MNIST Dataset Analysis.mp4 -
9 -
6. Pooling Layers.mp4 -
10 -
21. Load Model and Do the Prediction.mp4 -
11 -
12. Load Dataset for Baseline Classifier.mp4 -
12 -
2. Working Principle of CNN.mp4 -
13 -
5. What is Back Propagation.mp4 -
14 -
8. Discovering Overfitting - Early Stopping.mp4 -
15 -
4. Download Humans or Horses Dataset Part 2.mp4 -
16 -
1.1 python-for-deep-learning-and-ai.zip -
17 -
2. Introduction to TensorFlow Datasets (TFDS).mp4 -
18 -
5. Use of Image Data Generator.mp4 -
19 -
8. Other Types of Data Augmentation.mp4 -
20 -
7. Activation Function.mp4 -
21 -
10. LeNet-5 Architecture Explained.mp4 -
22 -
5. Sample Data Load with ImageDataGenerator for Augmentation.mp4 -
23 -
8. Matplotlib Introduction Part 2.mp4 -
24 -
15. How to Calculate Number of Parameters in CNN and FCN.mp4 -
25 -
12. GoogLeNet (Inception V1) Architecture Explained.mp4 -
26 -
2. Load Flowers Dataset for Classification.mp4 -
27 -
4. Feature Maps.mp4 -
28 -
13. Code Along in Python Part 4.mp4 -
29 -
7. Model Summary and Training.mp4 -
30 -
7. Matplotlib Introduction Part 1.mp4 -
31 -
3. Overview of NLP Tools.mp4 -
32 -
1. What is Convolutional Neural Network.mp4 -
33 -
7. Steps to Build Neural Network.mp4 -
34 -
13. Make CNN Model with VGG16 Transfer Learning.mp4 -
35 -
7. How to Calculate Number of Parameters in CNN.mp4 -
36 -
15. Train Any Model for Transfer Learning.mp4 -
37 -
3. Fashion MNIST Dataset Download.mp4 -
38 -
11. CNN Parameter Calculations Part 3.mp4 -
39 -
14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4 -
40 -
14. Neural Network Model Building.mp4 -
41 -
8. Building CNN Model.mp4 -
42 -
10. Seaborn Introduction Part 2.mp4 -
43 -
11. Code Along in Python Part 2.mp4 -
44 -
12. Model Training.mp4 -
45 -
1. Transfer Learning Introduction.mp4 -
46 -
13. RestNet Architecture Explained.mp4 -
47 -
16. Model Training.mp4 -
48 -
3. Download Humans or Horses Dataset Part 1.mp4 -
49 -
6. Random Rotation Augmentation.mp4 -
50 -
9. CNN Architectures Comparison.mp4 -
51 -
2. Multi-Layer Perceptron.mp4 -
52 -
11. Store Data in Local Directory.mp4 -
53 -
7. CNN Introduction.mp4 -
54 -
14. Image Class Prediction.mp4 -
55 -
6. Optimizers in Deep Learning.mp4 -
56 -
5. Preparing Data with Image Data Generator.mp4 -
57 -
11. import VGG16 from Keras.mp4 -
58 -
19. Prediction on Real-Life Data.mp4 -
59 -
9. Data Visualization Part 1.mp4 -
60 -
3. Download Flowers Data.mp4 -
61 -
6. Pandas Introduction.mp4 -
62 -
15. Model Summary Explanation.mp4 -
63 -
4. Flowers Data Visualization.mp4 -
64 -
4. What is Data Augmentation [Theory].mp4 -
65 -
6. Baseline CNN Model Building.mp4 -
66 -
8. Baseline CNN Model Training.mp4 -
67 -
7. Random Shift Augmentation.mp4 -
68 -
8. Text Preprocessing.mp4 -
69 -
2. L1, L2 and Early Stopping Regularization.mp4 -
70 -
9. CNN Parameter Calculation.mp4 -
71 -
9. Model Save and Load for Prediction.mp4 -
72 -
12. Code Along in Python Part 3.mp4 -
73 -
1. Overview of Image Classification using CNNs.mp4 -
74 -
4. Unsupervised Learning.mp4 -
75 -
10. CNN Parameter Calculations Part 2.mp4 -
76 -
1. Introduction to Computer Vision with Deep Learning.mp4 -
77 -
1. What is Overfitting.mp4 -
78 -
3. How Dropout and Batch Normalization Prevents Overfitting.mp4 -
79 -
19. Regularized CNN Model Building and Training.mp4 -
80 -
10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4 -
81 -
10. Pair Plot.mp4 -
82 -
13. Building Baseline CNN Classifier.mp4 -
83 -
9. Train Model with TFDS Data Without Saving Locally Part 1.mp4 -
84 -
16. Save and Load Model with Class Names.mp4 -
85 -
4. Activation Function.mp4 -
86 -
4. Numpy Introduction Part 1.mp4 -
87 -
16. Model Training and Layers Analysis.mp4 -
88 -
2. What are Key NLP Techniques.mp4 -
89 -
10. Train Model with TFDS Data Without Saving Locally Part 2.mp4 -
90 -
6. L2 Regularization.mp4 -
91 -
1. Machine Learning Process Introduction.mp4 -
92 -
2. Python Introduction Part 2.mp4 -
93 -
3. Support Vector Machine - SVM.mp4 -
94 -
12. Import Neural Networks APIs.mp4 -
95 -
6. Deep Neural Network Model Building.mp4 -
96 -
11. Data Preprocessing.mp4 -
97 -
5. Numpy Introduction Part 2.mp4 -
98 -
12. TF-IDF Vectorization.mp4 -
99 -
3. Python Introduction Part 3.mp4 -
100 -
2. Logistic Regression.mp4 -
101 -
10. Code Along in Python Part 1.mp4 -
102 -
9. Feature Engineering.mp4 -
103 -
1. Python Introduction Part 1.mp4 -
104 -
7. What is Neural Network.mp4 -
105 -
9. All Types of Augmentation at Once.mp4 -
106 -
8. Dropout.mp4 -
107 -
13. Model Load and Save.mp4 -
108 -
1. Classical Machine Learning Introduction.mp4 -
109 -
10. Deep Learning Tools.mp4 -
110 -
8. Model Evaluation.mp4 -
111 -
9. Seaborn Introduction Part 1.mp4 -
112 -
6. What is Deep Learning and ML.mp4 -
113 -
9. Application of Deep Learning.mp4 -
114 -
2. 5 Steps of Computer Vision Model Building.mp4 -
115 -
5. Bag of Words - The Simples Word Embedding Technique.mp4 -
116 -
8. Customer Churn Dataset Loading.mp4 -
117 -
17. Model Training and Validation Accuracy Plot.mp4 -
118 -
5. Train Test Split for Data.mp4 -
119 -
12. Data Augmentation for Training.mp4 -
120 -
3. Supervised Learning.mp4 -
121 -
20. Training Log Analysis.mp4 -
122 -
4. Decision Tree.mp4 -
123 -
8. How Deep Learning Process Works.mp4 -
124 -
18. Model Save and Load.mp4 -
125 -
14. Model Training for Better Accuracy.mp4 -
126 -
1. Introduction to NLP.mp4 -
127 -
13. Model Evaluation and Prediction on Real Data.mp4 -
128 -
14. Model Load and Store.mp4 -
129 -
11. MLops with AWS.mp4 -
130 -
13. How to Get Input Shape and Class Weights.mp4 -
131 -
1. What is Neuron.mp4 -
132 -
6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4 -
133 -
2. Types of Machine Learning.mp4 -
134 -
4. Common Challenges in NLP.mp4 -
135 -
7. L1 Regularization.mp4 -
136 -
7. Load Spam Dataset.mp4 -
137 -
18. Building Dataset for Regularized CNN.mp4 -
138 -
5. Random Forest.mp4 -
139 -
5. Reinforcement Learning.mp4 -
140 -
17. Model Evaluation.mp4 -
141 -
22. CNN Model Visualization.mp4 -
142 -
3. Shallow vs Deep Neural Networks.mp4 -
143 -
9. ROC-AUC Curve.mp4 -
144 -
11. Train Test Split.mp4 -
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14. MobileNet Architecture Explained.mp4 -
121.3 MB
TutsNode.net.txt -
63 bytes
[TGx]Downloaded from torrentgalaxy.to .txt -
585 bytes
0 -
688.4 KB
3. Convolutional Filters.mp4 -
114.0 MB
1 -
978.9 KB
10. Data Visualization Part 2.mp4 -
107.3 MB
2 -
767.2 KB
15. EfficientNet Architecture Explained.mp4 -
104.3 MB
3 -
713.5 KB
1. Jupyter Notebook Introduction.mp4 -
103.1 MB
4 -
939.7 KB
5. Padding and Strides.mp4 -
102.3 MB
5 -
683.9 KB
11. AlexNet Architecture Explained.mp4 -
98.7 MB
6 -
287.6 KB
17. Online Prediction of Flowers Classes.mp4 -
96.9 MB
7 -
75.5 KB
6. Data Display in Subplots Matrix.mp4 -
89.2 MB
8 -
796.9 KB
4. Fashion MNIST Dataset Analysis.mp4 -
87.8 MB
9 -
248.5 KB
6. Pooling Layers.mp4 -
86.5 MB
10 -
503.1 KB
21. Load Model and Do the Prediction.mp4 -
83.4 MB
11 -
610.1 KB
12. Load Dataset for Baseline Classifier.mp4 -
82.9 MB
12 -
62.2 KB
2. Working Principle of CNN.mp4 -
80.2 MB
13 -
811.9 KB
5. What is Back Propagation.mp4 -
79.4 MB
14 -
613.1 KB
8. Discovering Overfitting - Early Stopping.mp4 -
77.5 MB
15 -
467.3 KB
4. Download Humans or Horses Dataset Part 2.mp4 -
76.0 MB
16 -
998.4 KB
1.1 python-for-deep-learning-and-ai.zip -
74.7 MB
17 -
338.6 KB
2. Introduction to TensorFlow Datasets (TFDS).mp4 -
74.6 MB
18 -
362.4 KB
5. Use of Image Data Generator.mp4 -
73.4 MB
19 -
655.3 KB
8. Other Types of Data Augmentation.mp4 -
73.3 MB
20 -
674.5 KB
7. Activation Function.mp4 -
72.7 MB
21 -
353.1 KB
10. LeNet-5 Architecture Explained.mp4 -
71.1 MB
22 -
900.0 KB
5. Sample Data Load with ImageDataGenerator for Augmentation.mp4 -
71.0 MB
23 -
976.6 KB
8. Matplotlib Introduction Part 2.mp4 -
70.1 MB
24 -
926.5 KB
15. How to Calculate Number of Parameters in CNN and FCN.mp4 -
68.7 MB
25 -
332.6 KB
12. GoogLeNet (Inception V1) Architecture Explained.mp4 -
68.4 MB
26 -
626.2 KB
2. Load Flowers Dataset for Classification.mp4 -
68.0 MB
27 -
15.7 KB
4. Feature Maps.mp4 -
66.9 MB
28 -
125.5 KB
13. Code Along in Python Part 4.mp4 -
66.7 MB
29 -
336.7 KB
7. Model Summary and Training.mp4 -
65.0 MB
30 -
31.5 KB
7. Matplotlib Introduction Part 1.mp4 -
64.8 MB
31 -
244.9 KB
3. Overview of NLP Tools.mp4 -
64.5 MB
32 -
483.8 KB
1. What is Convolutional Neural Network.mp4 -
64.2 MB
33 -
823.6 KB
7. Steps to Build Neural Network.mp4 -
64.1 MB
34 -
942.6 KB
13. Make CNN Model with VGG16 Transfer Learning.mp4 -
63.9 MB
35 -
86.1 KB
7. How to Calculate Number of Parameters in CNN.mp4 -
63.8 MB
36 -
191.6 KB
15. Train Any Model for Transfer Learning.mp4 -
63.3 MB
37 -
744.7 KB
3. Fashion MNIST Dataset Download.mp4 -
63.2 MB
38 -
822.2 KB
11. CNN Parameter Calculations Part 3.mp4 -
61.4 MB
39 -
589.0 KB
14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4 -
61.3 MB
40 -
695.2 KB
14. Neural Network Model Building.mp4 -
60.9 MB
41 -
130.8 KB
8. Building CNN Model.mp4 -
60.7 MB
42 -
332.8 KB
10. Seaborn Introduction Part 2.mp4 -
59.7 MB
43 -
285.6 KB
11. Code Along in Python Part 2.mp4 -
59.1 MB
44 -
871.7 KB
12. Model Training.mp4 -
58.9 MB
45 -
62.3 KB
1. Transfer Learning Introduction.mp4 -
57.4 MB
46 -
581.5 KB
13. RestNet Architecture Explained.mp4 -
56.8 MB
47 -
162.0 KB
16. Model Training.mp4 -
56.3 MB
48 -
744.3 KB
3. Download Humans or Horses Dataset Part 1.mp4 -
56.2 MB
49 -
868.8 KB
6. Random Rotation Augmentation.mp4 -
55.8 MB
50 -
182.2 KB
9. CNN Architectures Comparison.mp4 -
55.6 MB
51 -
447.7 KB
2. Multi-Layer Perceptron.mp4 -
55.1 MB
52 -
913.5 KB
11. Store Data in Local Directory.mp4 -
53.1 MB
53 -
952.4 KB
7. CNN Introduction.mp4 -
53.0 MB
54 -
992.1 KB
14. Image Class Prediction.mp4 -
52.3 MB
55 -
702.3 KB
6. Optimizers in Deep Learning.mp4 -
52.1 MB
56 -
949.9 KB
5. Preparing Data with Image Data Generator.mp4 -
51.2 MB
57 -
866.5 KB
11. import VGG16 from Keras.mp4 -
51.0 MB
58 -
1007.5 KB
19. Prediction on Real-Life Data.mp4 -
50.9 MB
59 -
62.1 KB
9. Data Visualization Part 1.mp4 -
50.2 MB
60 -
803.4 KB
3. Download Flowers Data.mp4 -
50.0 MB
61 -
1021.3 KB
6. Pandas Introduction.mp4 -
49.6 MB
62 -
453.1 KB
15. Model Summary Explanation.mp4 -
48.8 MB
63 -
208.9 KB
4. Flowers Data Visualization.mp4 -
48.7 MB
64 -
316.5 KB
4. What is Data Augmentation [Theory].mp4 -
48.6 MB
65 -
440.7 KB
6. Baseline CNN Model Building.mp4 -
46.4 MB
66 -
655.5 KB
8. Baseline CNN Model Training.mp4 -
46.3 MB
67 -
670.9 KB
7. Random Shift Augmentation.mp4 -
45.9 MB
68 -
61.9 KB
8. Text Preprocessing.mp4 -
45.8 MB
69 -
230.6 KB
2. L1, L2 and Early Stopping Regularization.mp4 -
44.8 MB
70 -
208.1 KB
9. CNN Parameter Calculation.mp4 -
44.8 MB
71 -
221.8 KB
9. Model Save and Load for Prediction.mp4 -
44.7 MB
72 -
322.6 KB
12. Code Along in Python Part 3.mp4 -
44.1 MB
73 -
925.3 KB
1. Overview of Image Classification using CNNs.mp4 -
44.1 MB
74 -
963.6 KB
4. Unsupervised Learning.mp4 -
43.2 MB
75 -
778.4 KB
10. CNN Parameter Calculations Part 2.mp4 -
43.2 MB
76 -
868.5 KB
1. Introduction to Computer Vision with Deep Learning.mp4 -
43.0 MB
77 -
33.9 KB
1. What is Overfitting.mp4 -
42.8 MB
78 -
244.6 KB
3. How Dropout and Batch Normalization Prevents Overfitting.mp4 -
42.7 MB
79 -
277.5 KB
19. Regularized CNN Model Building and Training.mp4 -
42.4 MB
80 -
564.3 KB
10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4 -
42.4 MB
81 -
624.9 KB
10. Pair Plot.mp4 -
41.9 MB
82 -
64.8 KB
13. Building Baseline CNN Classifier.mp4 -
41.6 MB
83 -
392.7 KB
9. Train Model with TFDS Data Without Saving Locally Part 1.mp4 -
41.3 MB
84 -
718.5 KB
16. Save and Load Model with Class Names.mp4 -
40.4 MB
85 -
630.1 KB
4. Activation Function.mp4 -
40.3 MB
86 -
689.7 KB
4. Numpy Introduction Part 1.mp4 -
40.1 MB
87 -
930.0 KB
16. Model Training and Layers Analysis.mp4 -
39.9 MB
88 -
95.4 KB
2. What are Key NLP Techniques.mp4 -
39.6 MB
89 -
434.7 KB
10. Train Model with TFDS Data Without Saving Locally Part 2.mp4 -
38.5 MB
90 -
561.8 KB
6. L2 Regularization.mp4 -
38.3 MB
91 -
713.2 KB
1. Machine Learning Process Introduction.mp4 -
38.0 MB
92 -
19.6 KB
2. Python Introduction Part 2.mp4 -
37.8 MB
93 -
176.4 KB
3. Support Vector Machine - SVM.mp4 -
37.7 MB
94 -
345.2 KB
12. Import Neural Networks APIs.mp4 -
37.1 MB
95 -
967.8 KB
6. Deep Neural Network Model Building.mp4 -
36.5 MB
96 -
477.4 KB
11. Data Preprocessing.mp4 -
36.4 MB
97 -
638.7 KB
5. Numpy Introduction Part 2.mp4 -
36.3 MB
98 -
713.4 KB
12. TF-IDF Vectorization.mp4 -
34.7 MB
99 -
295.8 KB
3. Python Introduction Part 3.mp4 -
34.7 MB
100 -
353.8 KB
2. Logistic Regression.mp4 -
34.4 MB
101 -
583.3 KB
10. Code Along in Python Part 1.mp4 -
34.4 MB
102 -
657.8 KB
9. Feature Engineering.mp4 -
33.7 MB
103 -
302.4 KB
1. Python Introduction Part 1.mp4 -
33.6 MB
104 -
458.0 KB
7. What is Neural Network.mp4 -
33.2 MB
105 -
798.5 KB
9. All Types of Augmentation at Once.mp4 -
32.7 MB
106 -
348.6 KB
8. Dropout.mp4 -
32.4 MB
107 -
652.8 KB
13. Model Load and Save.mp4 -
32.1 MB
108 -
941.6 KB
1. Classical Machine Learning Introduction.mp4 -
32.0 MB
109 -
19.4 KB
10. Deep Learning Tools.mp4 -
31.8 MB
110 -
227.7 KB
8. Model Evaluation.mp4 -
31.5 MB
111 -
505.4 KB
9. Seaborn Introduction Part 1.mp4 -
30.6 MB
112 -
425.1 KB
6. What is Deep Learning and ML.mp4 -
30.2 MB
113 -
812.3 KB
9. Application of Deep Learning.mp4 -
28.6 MB
114 -
398.0 KB
2. 5 Steps of Computer Vision Model Building.mp4 -
27.7 MB
115 -
332.2 KB
5. Bag of Words - The Simples Word Embedding Technique.mp4 -
27.3 MB
116 -
704.4 KB
8. Customer Churn Dataset Loading.mp4 -
26.0 MB
117 -
1023.5 KB
17. Model Training and Validation Accuracy Plot.mp4 -
26.0 MB
118 -
28.2 KB
5. Train Test Split for Data.mp4 -
25.8 MB
119 -
185.4 KB
12. Data Augmentation for Training.mp4 -
25.6 MB
120 -
381.2 KB
3. Supervised Learning.mp4 -
25.5 MB
121 -
468.2 KB
20. Training Log Analysis.mp4 -
25.5 MB
122 -
470.0 KB
4. Decision Tree.mp4 -
25.5 MB
123 -
506.5 KB
8. How Deep Learning Process Works.mp4 -
23.9 MB
124 -
55.6 KB
18. Model Save and Load.mp4 -
23.6 MB
125 -
384.4 KB
14. Model Training for Better Accuracy.mp4 -
23.3 MB
126 -
668.0 KB
1. Introduction to NLP.mp4 -
22.6 MB
127 -
457.2 KB
13. Model Evaluation and Prediction on Real Data.mp4 -
22.3 MB
128 -
753.1 KB
14. Model Load and Store.mp4 -
22.1 MB
129 -
954.0 KB
11. MLops with AWS.mp4 -
21.6 MB
130 -
367.5 KB
13. How to Get Input Shape and Class Weights.mp4 -
21.2 MB
131 -
825.0 KB
1. What is Neuron.mp4 -
20.9 MB
132 -
149.6 KB
6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4 -
20.0 MB
133 -
999.8 KB
2. Types of Machine Learning.mp4 -
19.3 MB
134 -
731.2 KB
4. Common Challenges in NLP.mp4 -
19.1 MB
135 -
887.0 KB
7. L1 Regularization.mp4 -
18.7 MB
136 -
265.9 KB
7. Load Spam Dataset.mp4 -
18.6 MB
137 -
449.8 KB
18. Building Dataset for Regularized CNN.mp4 -
17.5 MB
138 -
507.3 KB
5. Random Forest.mp4 -
17.5 MB
139 -
532.8 KB
5. Reinforcement Learning.mp4 -
16.3 MB
140 -
747.7 KB
17. Model Evaluation.mp4 -
16.1 MB
141 -
906.6 KB
22. CNN Model Visualization.mp4 -
14.3 MB
142 -
705.5 KB
3. Shallow vs Deep Neural Networks.mp4 -
13.8 MB
143 -
158.5 KB
9. ROC-AUC Curve.mp4 -
13.5 MB
144 -
554.6 KB
11. Train Test Split.mp4 -
8.7 MB
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