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文档列表
30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder.mp4
424.0 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network.mp4
349.4 MB
21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE.mp4
334.7 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model.mp4
272.9 MB
21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap.mp4
271.9 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN.mp4
263.6 MB
18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation.mp4
235.5 MB
8. Introduction to PyTorch/9. Loss Functions in PyTorch.mp4
233.6 MB
33. Build a Chatbot with Transformers/16. Loss with Label Smoothing.mp4
225.1 MB
27. Practical Recurrent Networks in PyTorch/6. Generating Text.mp4
186.5 MB
18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset.mp4
186.0 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2.mp4
184.7 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network.mp4
178.8 MB
34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code.mp4
168.9 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function.mp4
166.6 MB
1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do.mp4
163.8 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network.mp4
163.8 MB
27. Practical Recurrent Networks in PyTorch/5. Training the Network.mp4
159.0 MB
15. CNN Architectures/3. Residual Networks Part 2.mp4
158.7 MB
11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation.mp4
155.3 MB
8. Introduction to PyTorch/4. How PyTorch Works.mp4
154.6 MB
16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4.mp4
150.2 MB
19. Convolutional Networks Visualization/2. Processing the Model.mp4
149.4 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters.mp4
149.1 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1.mp4
146.1 MB
36. BERT/5. Exploring Transformers.mp4
143.2 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1.mp4
142.7 MB
33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2.mp4
141.2 MB
20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1.mp4
140.3 MB
19. Convolutional Networks Visualization/3. Visualizing the Feature Maps.mp4
139.7 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN.mp4
137.4 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3.mp4
137.4 MB
24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4.mp4
137.3 MB
28. Saving and Loading Models/1. Saving and Loading Part 1.mp4
137.0 MB
24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2.mp4
134.1 MB
2. Loss Functions/10. Triplet Ranking Loss.mp4
131.8 MB
20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3.mp4
129.9 MB
20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6.mp4
129.8 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing.mp4
129.8 MB
33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3.mp4
129.5 MB
15. CNN Architectures/2. Residual Networks Part 1.mp4
128.2 MB
18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results.mp4
124.2 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1.mp4
123.9 MB
33. Build a Chatbot with Transformers/14. Transformer.mp4
122.8 MB
34. Universal Transformers/3. Transformers for other tasks.mp4
118.3 MB
27. Practical Recurrent Networks in PyTorch/4. Creating the Network.mp4
117.5 MB
25. Recurrent Neural Networks/7. LSTMs.mp4
117.1 MB
1. How Neural Networks and Backpropagation Works/4. The Perceptron.mp4
116.3 MB
33. Build a Chatbot with Transformers/19. Evaluation Function.mp4
115.1 MB
7. Weight Initialization/3. Xavier Initialization.mp4
115.0 MB
27. Practical Recurrent Networks in PyTorch/2. Processing the Text.mp4
113.9 MB
37. Vision Transformers/3. Vision Transformer Part 3.mp4
111.6 MB
24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3.mp4
111.0 MB
20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5.mp4
110.1 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters.mp4
109.9 MB
22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2.mp4
108.8 MB
16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3.mp4
108.2 MB
18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data.mp4
106.7 MB
22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1.mp4
106.1 MB
33. Build a Chatbot with Transformers/18. Training Function.mp4
105.4 MB
4. Regularization and Normalization/6. Batch Normalization.mp4
105.2 MB
13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs.mp4
104.3 MB
8. Introduction to PyTorch/3. Installing PyTorch and an Introduction.mp4
104.1 MB
11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations.mp4
103.6 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2.mp4
102.2 MB
18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network.mp4
101.7 MB
28. Saving and Loading Models/2. Saving and Loading Part 2.mp4
101.3 MB
32. Transformers/3. Positional Encoding.mp4
100.6 MB
15. CNN Architectures/5. Densely Connected Networks.mp4
99.8 MB
33. Build a Chatbot with Transformers/20. Main Function and User Evaluation.mp4
97.8 MB
22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3.mp4
97.7 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder.mp4
97.2 MB
33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5.mp4
96.9 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function.mp4
95.0 MB
38. GPT/1. GPT Part 1.mp4
93.2 MB
8. Introduction to PyTorch/5. Torch Tensors - Part 1.mp4
91.3 MB
33. Build a Chatbot with Transformers/12. Encoder Layer.mp4
90.9 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class.mp4
90.1 MB
16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2.mp4
89.9 MB
5. Optimization/13. AMSGrad.mp4
89.8 MB
37. Vision Transformers/1. Vision Transformer Part 1.mp4
89.4 MB
21. Autoencoders and Variational Autoencoders/7. Deep Fake.mp4
89.4 MB
11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation.mp4
89.3 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder.mp4
89.0 MB
33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1.mp4
87.4 MB
29. Sequence Modelling/1. Sequence Modeling.mp4
85.5 MB
33. Build a Chatbot with Transformers/7. Embeddings.mp4
85.2 MB
13. Convolutional Neural Networks/8. Activation, Pooling and FC.mp4
84.6 MB
20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2.mp4
84.6 MB
33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3.mp4
83.9 MB
5. Optimization/9. Adam Optimization.mp4
81.5 MB
2. Loss Functions/2. L1 Loss (MAE).mp4
81.0 MB
20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8.mp4
80.9 MB
8. Introduction to PyTorch/8. Automatic Differentiation.mp4
80.1 MB
33. Build a Chatbot with Transformers/6. Data Loading and Masking.mp4
79.5 MB
5. Optimization/11. Weight Decay.mp4
79.3 MB
33. Build a Chatbot with Transformers/15. AdamWarmup.mp4
78.9 MB
32. Transformers/15. Dropout.mp4
78.9 MB
4. Regularization and Normalization/3. Dropout.mp4
78.9 MB
8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4
78.7 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction.mp4
78.1 MB
19. Convolutional Networks Visualization/1. Data and the Model.mp4
78.0 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation.mp4
77.7 MB
26. Word Embeddings/1. What are Word Embeddings.mp4
76.2 MB
10. Visualize the Learning Process/5. Visualize Learning Part 5.mp4
75.1 MB
16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1.mp4
75.0 MB
17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication.mp4
74.4 MB
11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters.mp4
74.0 MB
21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders.mp4
73.6 MB
20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7.mp4
73.1 MB
27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters.mp4
72.9 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate.mp4
72.7 MB
23. Neural Style Transfer/3. NST Theory Part 3.mp4
72.5 MB
11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function.mp4
71.8 MB
8. Introduction to PyTorch/6. Torch Tensors - Part 2.mp4
71.2 MB
2. Loss Functions/9. Hinge Loss.mp4
70.7 MB
25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem.mp4
70.1 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset.mp4
69.4 MB
8. Introduction to PyTorch/10. Weight Initialization in PyTorch.mp4
69.1 MB
32. Transformers/2. Input Embeddings.mp4
69.0 MB
10. Visualize the Learning Process/6. Visualize Learning Part 6.mp4
67.5 MB
24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1.mp4
66.9 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay.mp4
65.9 MB
2. Loss Functions/8. Contrastive Loss.mp4
65.7 MB
33. Build a Chatbot with Transformers/13. Decoder Layer.mp4
65.3 MB
25. Recurrent Neural Networks/4. Backpropagation Through Time.mp4
64.6 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset.mp4
63.7 MB
15. CNN Architectures/7. Seperable Convolutions.mp4
63.4 MB
33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1.mp4
63.4 MB
7. Weight Initialization/2. What happens when all weights are initialized to the same value.mp4
62.9 MB
27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary.mp4
62.8 MB
11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network.mp4
61.8 MB
32. Transformers/4. MultiHead Attention Part 1.mp4
61.2 MB
20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12.mp4
61.1 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2.mp4
59.7 MB
35. Google Colab and Gradient Accumulation/2. Gradient Accumulation.mp4
59.6 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions.mp4
58.9 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images.mp4
58.4 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization.mp4
58.1 MB
8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks.mp4
57.9 MB
26. Word Embeddings/5. Word Embeddings in PyTorch.mp4
55.8 MB
20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11.mp4
55.4 MB
28. Saving and Loading Models/3. Saving and Loading Part 3.mp4
55.4 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset.mp4
55.1 MB
23. Neural Style Transfer/1. NST Theory Part 1.mp4
55.1 MB
5. Optimization/12. Decoupling Weight Decay.mp4
54.8 MB
1. How Neural Networks and Backpropagation Works/6. The Forward Propagation.mp4
54.8 MB
13. Convolutional Neural Networks/3. Filters and Features.mp4
54.5 MB
25. Recurrent Neural Networks/2. Vanilla RNNs.mp4
54.1 MB
33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2.mp4
53.9 MB
38. GPT/5. Technical Details of GPT.mp4
53.9 MB
36. BERT/4. Fine-tuning BERT.mp4
53.1 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details.mp4
52.8 MB
18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network.mp4
52.5 MB
1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks.mp4
52.4 MB
5. Optimization/1. Batch Gradient Descent.mp4
51.8 MB
11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model.mp4
49.5 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network.mp4
49.4 MB
32. Transformers/1. Introduction to Transformers.mp4
49.0 MB
13. Convolutional Neural Networks/11. CNN Characteristics.mp4
48.1 MB
32. Transformers/5. MultiHead Attention Part 2.mp4
48.1 MB
4. Regularization and Normalization/7. Layer Normalization.mp4
47.7 MB
38. GPT/2. GPT Part 2.mp4
47.6 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action.mp4
47.5 MB
2. Loss Functions/4. Binary Cross Entropy Loss.mp4
47.1 MB
24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer.mp4
47.0 MB
2. Loss Functions/6. Softmax Function.mp4
46.9 MB
15. CNN Architectures/1. CNN Architectures Part 1.mp4
46.0 MB
33. Build a Chatbot with Transformers/17. Defining the Model.mp4
45.8 MB
38. GPT/3. Zero-Shot Predictions with GPT.mp4
45.5 MB
5. Optimization/5. Exponentially Weighted Average Implementation.mp4
45.2 MB
33. Build a Chatbot with Transformers/11. Feed Forward Implementation.mp4
45.0 MB
36. BERT/3. Next Sentence Prediction.mp4
44.7 MB
21. Autoencoders and Variational Autoencoders/1. Autoencoders.mp4
44.1 MB
1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning.mp4
43.8 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions.mp4
43.4 MB
1. How Neural Networks and Backpropagation Works/5. Gradient Descent.mp4
42.6 MB
29. Sequence Modelling/4. How Attention Mechanisms Work.mp4
42.1 MB
15. CNN Architectures/6. Squeeze-Excite Networks.mp4
41.5 MB
38. GPT/4. Byte-Pair Encoding.mp4
41.2 MB
5. Optimization/8. RMSProp.mp4
40.9 MB
3. Activation Functions/8. Mish Activation.mp4
40.0 MB
13. Convolutional Neural Networks/1. Prerequisite Filters.mp4
38.2 MB
17. Transposed Convolutions/3. Transposed Convolutions.mp4
37.8 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN.mp4
37.6 MB
37. Vision Transformers/2. Vision Transformer Part 2.mp4
37.0 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts.mp4
36.9 MB
23. Neural Style Transfer/2. NST Theory Part 2.mp4
36.9 MB
29. Sequence Modelling/2. Image Captioning.mp4
36.4 MB
36. BERT/1. What is BERT and its structure.mp4
36.4 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results.mp4
35.5 MB
4. Regularization and Normalization/2. L1 and L2 Regularization.mp4
35.1 MB
35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab.mp4
34.8 MB
32. Transformers/12. Cross Entropy Loss.mp4
34.3 MB
10. Visualize the Learning Process/7. Neural Networks Playground.mp4
34.1 MB
33. Build a Chatbot with Transformers/21. Action.mp4
33.8 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details.mp4
33.5 MB
17. Transposed Convolutions/1. Introduction to Transposed Convolutions.mp4
32.5 MB
5. Optimization/6. Bias Correction in Exponentially Weighted Averages.mp4
32.4 MB
38. GPT/6. Playing with HuggingFace models.mp4
31.7 MB
21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders.mp4
31.5 MB
13. Convolutional Neural Networks/5. More on Convolutions.mp4
31.4 MB
1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1.mp4
30.8 MB
15. CNN Architectures/8. Transfer Learning.mp4
30.7 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence.mp4
30.6 MB
32. Transformers/16. Learning Rate Warmup.mp4
30.5 MB
2. Loss Functions/3. Huber Loss.mp4
30.0 MB
32. Transformers/7. Residual Learning.mp4
29.4 MB
13. Convolutional Neural Networks/7. A Tool for Convolution Visualization.mp4
29.3 MB
1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2.mp4
29.2 MB
11. Implementing a Neural Network from Scratch with Numpy/5. Prediction.mp4
29.1 MB
10. Visualize the Learning Process/3. Visualize Learning Part 3.mp4
28.7 MB
13. Convolutional Neural Networks/14. Softmax with Temperature.mp4
28.7 MB
5. Optimization/7. Momentum.mp4
28.7 MB
32. Transformers/10. Masked MultiHead Attention.mp4
28.0 MB
3. Activation Functions/6. Gated Linear Units (GLU).mp4
27.8 MB
4. Regularization and Normalization/8. Group Normalization.mp4
27.7 MB
4. Regularization and Normalization/1. Overfitting.mp4
27.5 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation.mp4
27.5 MB
25. Recurrent Neural Networks/9. GRUs.mp4
27.4 MB
20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4.mp4
27.0 MB
2. Loss Functions/7. KL divergence Loss.mp4
26.6 MB
20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10.mp4
26.5 MB
13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them.mp4
26.3 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate.mp4
25.9 MB
2. Loss Functions/5. Cross Entropy Loss.mp4
25.9 MB
10. Visualize the Learning Process/1. Visualize Learning Part 1.mp4
25.6 MB
32. Transformers/13. KL Divergence Loss.mp4
24.7 MB
11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation.mp4
24.5 MB
36. BERT/2. Masked Language Modelling.mp4
24.2 MB
5. Optimization/4. Exponentially Weighted Average Intuition.mp4
24.0 MB
3. Activation Functions/1. Why we need activation functions.mp4
23.5 MB
34. Universal Transformers/1. Universal Transformers.mp4
22.9 MB
32. Transformers/8. Layer Normalization.mp4
22.8 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing.mp4
22.8 MB
25. Recurrent Neural Networks/10. CNN-LSTM.mp4
22.5 MB
13. Convolutional Neural Networks/4. Convolution over Volume Animation.mp4
22.3 MB
3. Activation Functions/4. ReLU and PReLU.mp4
21.8 MB
33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4.mp4
21.3 MB
3. Activation Functions/2. Sigmoid Activation.mp4
21.1 MB
10. Visualize the Learning Process/4. Visualize Learning Part 4.mp4
21.1 MB
2. Loss Functions/1. Mean Squared Error (MSE).mp4
20.8 MB
7. Weight Initialization/1. Normal Distribution.mp4
19.6 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model.mp4
19.6 MB
25. Recurrent Neural Networks/1. Why do we need RNNs.mp4
19.5 MB
13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs.mp4
19.1 MB
5. Optimization/2. Stochastic Gradient Descent.mp4
19.0 MB
20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9.mp4
18.6 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4
18.5 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image.mp4
18.3 MB
29. Sequence Modelling/3. Attention Mechanisms.mp4
17.3 MB
32. Transformers/9. Feed Forward.mp4
16.3 MB
13. Convolutional Neural Networks/9. CNN Visualization.mp4
16.2 MB
25. Recurrent Neural Networks/3. Quiz Solution Discussion.mp4
16.1 MB
25. Recurrent Neural Networks/8. Bidirectional RNNs.mp4
15.8 MB
4. Regularization and Normalization/4. DropConnect.mp4
14.9 MB
3. Activation Functions/3. Tanh Activation.mp4
14.5 MB
4. Regularization and Normalization/5. Normalization.mp4
14.2 MB
21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders.mp4
14.1 MB
13. Convolutional Neural Networks/10. Important formulas.mp4
14.0 MB
15. CNN Architectures/4. CNN Architectures Part 2.mp4
14.0 MB
7. Weight Initialization/4. He Norm Initialization.mp4
14.0 MB
32. Transformers/14. Label Smoothing.mp4
13.9 MB
3. Activation Functions/7. Swish Activation.mp4
13.5 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training.mp4
13.5 MB
10. Visualize the Learning Process/2. Visualize Learning Part 2.mp4
12.8 MB
26. Word Embeddings/2. Visualizing Word Embeddings.mp4
12.8 MB
32. Transformers/11. MultiHead Attention in Decoder.mp4
11.6 MB
26. Word Embeddings/4. Word Embeddings Models.mp4
11.2 MB
3. Activation Functions/5. Exponentially Linear Units (ELU).mp4
11.2 MB
5. Optimization/10. SWATS - Switching from Adam to SGD.mp4
10.3 MB
32. Transformers/6. Concat and Linear.mp4
10.2 MB
25. Recurrent Neural Networks/5. Stacked RNNs.mp4
8.1 MB
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25. Recurrent Neural Networks/8. Bidirectional RNNs-en_US.srt
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3. Activation Functions/7. Swish Activation-en_US.srt
5.3 kB
24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer-en_US.srt
5.3 kB
25. Recurrent Neural Networks/3. Quiz Solution Discussion-en_US.srt
5.2 kB
3. Activation Functions/1. Why we need activation functions-en_US.srt
5.2 kB
7. Weight Initialization/4. He Norm Initialization-en_US.srt
5.1 kB
3. Activation Functions/5. Exponentially Linear Units (ELU)-en_US.srt
5.0 kB
13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs-en_US.srt
4.8 kB
15. CNN Architectures/4. CNN Architectures Part 2-en_US.srt
4.7 kB
13. Convolutional Neural Networks/4. Convolution over Volume Animation-en_US.srt
4.7 kB
13. Convolutional Neural Networks/6. Quiz Solution Discussion-en_US.srt
4.6 kB
33. Build a Chatbot with Transformers/11. Feed Forward Implementation-en_US.srt
4.5 kB
26. Word Embeddings/2. Visualizing Word Embeddings-en_US.srt
4.4 kB
32. Transformers/9. Feed Forward-en_US.srt
4.4 kB
26. Word Embeddings/4. Word Embeddings Models-en_US.srt
4.3 kB
3. Activation Functions/3. Tanh Activation-en_US.srt
4.2 kB
6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate-en_US.srt
4.2 kB
32. Transformers/6. Concat and Linear-en_US.srt
4.1 kB
3. Activation Functions/6. Gated Linear Units (GLU)-en_US.srt
4.1 kB
33. Build a Chatbot with Transformers/21. Action-en_US.srt
4.0 kB
31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results-en_US.srt
3.8 kB
25. Recurrent Neural Networks/5. Stacked RNNs-en_US.srt
3.6 kB
32. Transformers/11. MultiHead Attention in Decoder-en_US.srt
3.5 kB
5. Optimization/3. Mini-Batch Gradient Descent-en_US.srt
3.5 kB
31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training-en_US.srt
3.4 kB
20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10-en_US.srt
2.9 kB
13. Convolutional Neural Networks/9. CNN Visualization-en_US.srt
2.8 kB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details-en_US.srt
2.7 kB
26. Word Embeddings/3. Measuring Word Embeddings-en_US.srt
2.6 kB
10. Visualize the Learning Process/2. Visualize Learning Part 2-en_US.srt
2.6 kB
4. Regularization and Normalization/4. DropConnect-en_US.srt
2.3 kB
5. Optimization/10. SWATS - Switching from Adam to SGD-en_US.srt
2.1 kB
20. YOLO Object Detection (Theory)/YOLO Code Note.html
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8. Introduction to PyTorch/1. CODE FOR THIS COURSE-en_US.srt
701 Bytes
1. How Neural Networks and Backpropagation Works/BEFORE STARTING...PLEASE READ THIS.html
630 Bytes
11. Implementing a Neural Network from Scratch with Numpy/Notebook for the following Lecture.html
532 Bytes
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/The MNIST Dataset.html
421 Bytes
2. Loss Functions/Softmax with Temperature Controlling your distribution.html
394 Bytes
4. Regularization and Normalization/Note on Weight Decay.html
354 Bytes
1. How Neural Networks and Backpropagation Works/Before Proceeding with the Backpropagation.html
341 Bytes
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/Download the Dataset.html
322 Bytes
13. Convolutional Neural Networks/Convolution over Volume Animation Resource.html
321 Bytes
27. Practical Recurrent Networks in PyTorch/Download the Dataset.html
312 Bytes
32. Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html
272 Bytes
33. Build a Chatbot with Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html
272 Bytes
34. Universal Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html
272 Bytes
37. Vision Transformers/SANITY CHECK ON PREVIOUS SECTIONS.html
272 Bytes
33. Build a Chatbot with Transformers/CODE.html
268 Bytes
4. Regularization and Normalization/DropBlock in CNNs.html
256 Bytes
30. Practical Sequence Modelling in PyTorch Chatbot Application/Download the Dataset.html
252 Bytes
7. Weight Initialization/Practical Weight Initialization Note.html
186 Bytes
2. Loss Functions/Practical Loss Functions Note.html
179 Bytes
2. Loss Functions/[Tutorialsplanet.NET].url
128 Bytes
8. Introduction to PyTorch/[Tutorialsplanet.NET].url
128 Bytes
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128 Bytes
18. Transfer Learning in PyTorch - Image Classification/[Tutorialsplanet.NET].url
128 Bytes
25. Recurrent Neural Networks/[Tutorialsplanet.NET].url
128 Bytes
32. Transformers/[Tutorialsplanet.NET].url
128 Bytes
15. CNN Architectures/Note on Residual Networks Implementation.html
109 Bytes
38. GPT/Implementation.html
87 Bytes
18. Transfer Learning in PyTorch - Image Classification/2. External URLs.txt
70 Bytes
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions-en_US.srt
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