[Tutorialsplanet.NET] Udemy - The Complete Neural Networks Bootcamp Theory, Applications

Creation Time	April 25, 2024, 10:48 a.m.
Last Access Time	Dec. 23, 2024, 12:30 a.m.
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Keywords	Tutorialsplanet Complete Neural Networks - Udemy Applications NET Bootcamp The Theory
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30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder.mp4 404.3 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network.mp4 333.2 MB
21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE.mp4 319.2 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model.mp4 260.3 MB
21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap.mp4 259.3 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN.mp4 251.4 MB
18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation.mp4 224.6 MB
8. Introduction to PyTorch/9. Loss Functions in PyTorch.mp4 222.8 MB
33. Build a Chatbot with Transformers/16. Loss with Label Smoothing.mp4 214.7 MB
27. Practical Recurrent Networks in PyTorch/6. Generating Text.mp4 177.8 MB
18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset.mp4 177.4 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2.mp4 176.1 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network.mp4 170.5 MB
34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code.mp4 161.1 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function.mp4 158.9 MB
1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do.mp4 156.2 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network.mp4 156.2 MB
27. Practical Recurrent Networks in PyTorch/5. Training the Network.mp4 151.6 MB
15. CNN Architectures/3. Residual Networks Part 2.mp4 151.4 MB
11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation.mp4 148.1 MB
8. Introduction to PyTorch/4. How PyTorch Works.mp4 147.4 MB
16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4.mp4 143.3 MB
19. Convolutional Networks Visualization/2. Processing the Model.mp4 142.5 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters.mp4 142.2 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1.mp4 139.3 MB
36. BERT/5. Exploring Transformers.mp4 136.6 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1.mp4 136.1 MB
33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2.mp4 134.6 MB
20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1.mp4 133.8 MB
19. Convolutional Networks Visualization/3. Visualizing the Feature Maps.mp4 133.3 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN.mp4 131.1 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3.mp4 131.0 MB
24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4.mp4 131.0 MB
28. Saving and Loading Models/1. Saving and Loading Part 1.mp4 130.6 MB
24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2.mp4 127.9 MB
2. Loss Functions/10. Triplet Ranking Loss.mp4 125.7 MB
20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3.mp4 123.9 MB
20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6.mp4 123.8 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing.mp4 123.8 MB
33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3.mp4 123.5 MB
15. CNN Architectures/2. Residual Networks Part 1.mp4 122.3 MB
18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results.mp4 118.4 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1.mp4 118.2 MB
33. Build a Chatbot with Transformers/14. Transformer.mp4 117.1 MB
34. Universal Transformers/3. Transformers for other tasks.mp4 112.8 MB
27. Practical Recurrent Networks in PyTorch/4. Creating the Network.mp4 112.1 MB
25. Recurrent Neural Networks/7. LSTMs.mp4 111.6 MB
1. How Neural Networks and Backpropagation Works/4. The Perceptron.mp4 110.9 MB
33. Build a Chatbot with Transformers/19. Evaluation Function.mp4 109.8 MB
7. Weight Initialization/3. Xavier Initialization.mp4 109.7 MB
27. Practical Recurrent Networks in PyTorch/2. Processing the Text.mp4 108.7 MB
37. Vision Transformers/3. Vision Transformer Part 3.mp4 106.4 MB
24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3.mp4 105.9 MB
20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5.mp4 105.0 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters.mp4 104.8 MB
22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2.mp4 103.8 MB
16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3.mp4 103.2 MB
18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data.mp4 101.8 MB
22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1.mp4 101.2 MB
33. Build a Chatbot with Transformers/18. Training Function.mp4 100.6 MB
4. Regularization and Normalization/6. Batch Normalization.mp4 100.3 MB
13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs.mp4 99.5 MB
8. Introduction to PyTorch/3. Installing PyTorch and an Introduction.mp4 99.3 MB
11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations.mp4 98.8 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2.mp4 97.5 MB
18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network.mp4 97.0 MB
28. Saving and Loading Models/2. Saving and Loading Part 2.mp4 96.6 MB
32. Transformers/3. Positional Encoding.mp4 96.0 MB
15. CNN Architectures/5. Densely Connected Networks.mp4 95.1 MB
33. Build a Chatbot with Transformers/20. Main Function and User Evaluation.mp4 93.3 MB
22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3.mp4 93.2 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder.mp4 92.7 MB
33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5.mp4 92.4 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function.mp4 90.6 MB
38. GPT/1. GPT Part 1.mp4 88.9 MB
8. Introduction to PyTorch/5. Torch Tensors - Part 1.mp4 87.1 MB
33. Build a Chatbot with Transformers/12. Encoder Layer.mp4 86.7 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class.mp4 86.0 MB
16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2.mp4 85.7 MB
5. Optimization/13. AMSGrad.mp4 85.6 MB
37. Vision Transformers/1. Vision Transformer Part 1.mp4 85.3 MB
21. Autoencoders and Variational Autoencoders/7. Deep Fake.mp4 85.3 MB
11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation.mp4 85.2 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder.mp4 84.9 MB
33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1.mp4 83.4 MB
29. Sequence Modelling/1. Sequence Modeling.mp4 81.6 MB
33. Build a Chatbot with Transformers/7. Embeddings.mp4 81.2 MB
13. Convolutional Neural Networks/8. Activation, Pooling and FC.mp4 80.7 MB
20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2.mp4 80.6 MB
33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3.mp4 80.1 MB
5. Optimization/9. Adam Optimization.mp4 77.8 MB
2. Loss Functions/2. L1 Loss (MAE).mp4 77.2 MB
20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8.mp4 77.2 MB
8. Introduction to PyTorch/8. Automatic Differentiation.mp4 76.4 MB
33. Build a Chatbot with Transformers/6. Data Loading and Masking.mp4 75.8 MB
5. Optimization/11. Weight Decay.mp4 75.6 MB
33. Build a Chatbot with Transformers/15. AdamWarmup.mp4 75.3 MB
32. Transformers/15. Dropout.mp4 75.2 MB
4. Regularization and Normalization/3. Dropout.mp4 75.2 MB
8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4 75.1 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction.mp4 74.4 MB
19. Convolutional Networks Visualization/1. Data and the Model.mp4 74.4 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation.mp4 74.1 MB
26. Word Embeddings/1. What are Word Embeddings.mp4 72.7 MB
10. Visualize the Learning Process/5. Visualize Learning Part 5.mp4 71.7 MB
16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1.mp4 71.5 MB
17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication.mp4 71.0 MB
11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters.mp4 70.5 MB
21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders.mp4 70.2 MB
20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7.mp4 69.7 MB
27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters.mp4 69.5 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate.mp4 69.4 MB
23. Neural Style Transfer/3. NST Theory Part 3.mp4 69.1 MB
11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function.mp4 68.5 MB
8. Introduction to PyTorch/6. Torch Tensors - Part 2.mp4 67.9 MB
2. Loss Functions/9. Hinge Loss.mp4 67.4 MB
25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem.mp4 66.9 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset.mp4 66.2 MB
8. Introduction to PyTorch/10. Weight Initialization in PyTorch.mp4 65.9 MB
32. Transformers/2. Input Embeddings.mp4 65.8 MB
10. Visualize the Learning Process/6. Visualize Learning Part 6.mp4 64.4 MB
24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1.mp4 63.8 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay.mp4 62.9 MB
2. Loss Functions/8. Contrastive Loss.mp4 62.7 MB
33. Build a Chatbot with Transformers/13. Decoder Layer.mp4 62.3 MB
25. Recurrent Neural Networks/4. Backpropagation Through Time.mp4 61.6 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset.mp4 60.7 MB
15. CNN Architectures/7. Seperable Convolutions.mp4 60.5 MB
33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1.mp4 60.4 MB
7. Weight Initialization/2. What happens when all weights are initialized to the same value.mp4 60.0 MB
27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary.mp4 59.9 MB
11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network.mp4 58.9 MB
32. Transformers/4. MultiHead Attention Part 1.mp4 58.3 MB
20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12.mp4 58.3 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2.mp4 56.9 MB
35. Google Colab and Gradient Accumulation/2. Gradient Accumulation.mp4 56.8 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions.mp4 56.2 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images.mp4 55.7 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization.mp4 55.4 MB
8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks.mp4 55.2 MB
26. Word Embeddings/5. Word Embeddings in PyTorch.mp4 53.2 MB
20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11.mp4 52.8 MB
28. Saving and Loading Models/3. Saving and Loading Part 3.mp4 52.8 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset.mp4 52.6 MB
23. Neural Style Transfer/1. NST Theory Part 1.mp4 52.5 MB
5. Optimization/12. Decoupling Weight Decay.mp4 52.3 MB
1. How Neural Networks and Backpropagation Works/6. The Forward Propagation.mp4 52.2 MB
13. Convolutional Neural Networks/3. Filters and Features.mp4 51.9 MB
25. Recurrent Neural Networks/2. Vanilla RNNs.mp4 51.6 MB
33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2.mp4 51.4 MB
38. GPT/5. Technical Details of GPT.mp4 51.4 MB
36. BERT/4. Fine-tuning BERT.mp4 50.7 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details.mp4 50.3 MB
18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network.mp4 50.0 MB
1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks.mp4 50.0 MB
5. Optimization/1. Batch Gradient Descent.mp4 49.4 MB
11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model.mp4 47.2 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network.mp4 47.1 MB
32. Transformers/1. Introduction to Transformers.mp4 46.7 MB
13. Convolutional Neural Networks/11. CNN Characteristics.mp4 45.9 MB
32. Transformers/5. MultiHead Attention Part 2.mp4 45.8 MB
4. Regularization and Normalization/7. Layer Normalization.mp4 45.5 MB
38. GPT/2. GPT Part 2.mp4 45.4 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action.mp4 45.3 MB
2. Loss Functions/4. Binary Cross Entropy Loss.mp4 44.9 MB
24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer.mp4 44.8 MB
2. Loss Functions/6. Softmax Function.mp4 44.7 MB
15. CNN Architectures/1. CNN Architectures Part 1.mp4 43.9 MB
33. Build a Chatbot with Transformers/17. Defining the Model.mp4 43.7 MB
38. GPT/3. Zero-Shot Predictions with GPT.mp4 43.4 MB
5. Optimization/5. Exponentially Weighted Average Implementation.mp4 43.2 MB
33. Build a Chatbot with Transformers/11. Feed Forward Implementation.mp4 42.9 MB
36. BERT/3. Next Sentence Prediction.mp4 42.6 MB
21. Autoencoders and Variational Autoencoders/1. Autoencoders.mp4 42.1 MB
1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning.mp4 41.8 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions.mp4 41.4 MB
1. How Neural Networks and Backpropagation Works/5. Gradient Descent.mp4 40.6 MB
29. Sequence Modelling/4. How Attention Mechanisms Work.mp4 40.2 MB
15. CNN Architectures/6. Squeeze-Excite Networks.mp4 39.6 MB
38. GPT/4. Byte-Pair Encoding.mp4 39.3 MB
5. Optimization/8. RMSProp.mp4 39.0 MB
3. Activation Functions/8. Mish Activation.mp4 38.1 MB
13. Convolutional Neural Networks/1. Prerequisite Filters.mp4 36.4 MB
17. Transposed Convolutions/3. Transposed Convolutions.mp4 36.1 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN.mp4 35.8 MB
37. Vision Transformers/2. Vision Transformer Part 2.mp4 35.3 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts.mp4 35.2 MB
23. Neural Style Transfer/2. NST Theory Part 2.mp4 35.2 MB
29. Sequence Modelling/2. Image Captioning.mp4 34.7 MB
36. BERT/1. What is BERT and its structure.mp4 34.7 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results.mp4 33.9 MB
4. Regularization and Normalization/2. L1 and L2 Regularization.mp4 33.5 MB
35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab.mp4 33.2 MB
32. Transformers/12. Cross Entropy Loss.mp4 32.7 MB
10. Visualize the Learning Process/7. Neural Networks Playground.mp4 32.5 MB
33. Build a Chatbot with Transformers/21. Action.mp4 32.2 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details.mp4 31.9 MB
17. Transposed Convolutions/1. Introduction to Transposed Convolutions.mp4 31.0 MB
5. Optimization/6. Bias Correction in Exponentially Weighted Averages.mp4 30.9 MB
38. GPT/6. Playing with HuggingFace models.mp4 30.2 MB
21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders.mp4 30.0 MB
13. Convolutional Neural Networks/5. More on Convolutions.mp4 30.0 MB
1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1.mp4 29.4 MB
15. CNN Architectures/8. Transfer Learning.mp4 29.2 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence.mp4 29.2 MB
32. Transformers/16. Learning Rate Warmup.mp4 29.1 MB
2. Loss Functions/3. Huber Loss.mp4 28.7 MB
32. Transformers/7. Residual Learning.mp4 28.0 MB
13. Convolutional Neural Networks/7. A Tool for Convolution Visualization.mp4 28.0 MB
1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2.mp4 27.8 MB
11. Implementing a Neural Network from Scratch with Numpy/5. Prediction.mp4 27.7 MB
10. Visualize the Learning Process/3. Visualize Learning Part 3.mp4 27.4 MB
13. Convolutional Neural Networks/14. Softmax with Temperature.mp4 27.3 MB
5. Optimization/7. Momentum.mp4 27.3 MB
32. Transformers/10. Masked MultiHead Attention.mp4 26.7 MB
3. Activation Functions/6. Gated Linear Units (GLU).mp4 26.5 MB
4. Regularization and Normalization/8. Group Normalization.mp4 26.5 MB
4. Regularization and Normalization/1. Overfitting.mp4 26.3 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation.mp4 26.2 MB
25. Recurrent Neural Networks/9. GRUs.mp4 26.1 MB
20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4.mp4 25.8 MB
2. Loss Functions/7. KL divergence Loss.mp4 25.4 MB
20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10.mp4 25.3 MB
13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them.mp4 25.1 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate.mp4 24.7 MB
2. Loss Functions/5. Cross Entropy Loss.mp4 24.7 MB
10. Visualize the Learning Process/1. Visualize Learning Part 1.mp4 24.4 MB
32. Transformers/13. KL Divergence Loss.mp4 23.6 MB
11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation.mp4 23.4 MB
36. BERT/2. Masked Language Modelling.mp4 23.1 MB
5. Optimization/4. Exponentially Weighted Average Intuition.mp4 22.9 MB
3. Activation Functions/1. Why we need activation functions.mp4 22.5 MB
34. Universal Transformers/1. Universal Transformers.mp4 21.8 MB
32. Transformers/8. Layer Normalization.mp4 21.8 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing.mp4 21.7 MB
25. Recurrent Neural Networks/10. CNN-LSTM.mp4 21.5 MB
13. Convolutional Neural Networks/4. Convolution over Volume Animation.mp4 21.3 MB
3. Activation Functions/4. ReLU and PReLU.mp4 20.8 MB
33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4.mp4 20.3 MB
3. Activation Functions/2. Sigmoid Activation.mp4 20.2 MB
10. Visualize the Learning Process/4. Visualize Learning Part 4.mp4 20.1 MB
2. Loss Functions/1. Mean Squared Error (MSE).mp4 19.8 MB
7. Weight Initialization/1. Normal Distribution.mp4 18.7 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model.mp4 18.7 MB
25. Recurrent Neural Networks/1. Why do we need RNNs.mp4 18.6 MB
13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs.mp4 18.2 MB
5. Optimization/2. Stochastic Gradient Descent.mp4 18.1 MB
20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9.mp4 17.7 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 17.7 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image.mp4 17.5 MB
29. Sequence Modelling/3. Attention Mechanisms.mp4 16.5 MB
32. Transformers/9. Feed Forward.mp4 15.5 MB
13. Convolutional Neural Networks/9. CNN Visualization.mp4 15.4 MB
25. Recurrent Neural Networks/3. Quiz Solution Discussion.mp4 15.4 MB
25. Recurrent Neural Networks/8. Bidirectional RNNs.mp4 15.0 MB
4. Regularization and Normalization/4. DropConnect.mp4 14.2 MB
3. Activation Functions/3. Tanh Activation.mp4 13.9 MB
4. Regularization and Normalization/5. Normalization.mp4 13.5 MB
21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders.mp4 13.4 MB
13. Convolutional Neural Networks/10. Important formulas.mp4 13.4 MB
15. CNN Architectures/4. CNN Architectures Part 2.mp4 13.4 MB
7. Weight Initialization/4. He Norm Initialization.mp4 13.3 MB
32. Transformers/14. Label Smoothing.mp4 13.2 MB
3. Activation Functions/7. Swish Activation.mp4 12.9 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training.mp4 12.8 MB
10. Visualize the Learning Process/2. Visualize Learning Part 2.mp4 12.2 MB
26. Word Embeddings/2. Visualizing Word Embeddings.mp4 12.2 MB
32. Transformers/11. MultiHead Attention in Decoder.mp4 11.1 MB
26. Word Embeddings/4. Word Embeddings Models.mp4 10.7 MB
3. Activation Functions/5. Exponentially Linear Units (ELU).mp4 10.6 MB
5. Optimization/10. SWATS - Switching from Adam to SGD.mp4 9.8 MB
32. Transformers/6. Concat and Linear.mp4 9.8 MB
25. Recurrent Neural Networks/5. Stacked RNNs.mp4 7.8 MB
5. Optimization/3. Mini-Batch Gradient Descent.mp4 6.9 MB
13. Convolutional Neural Networks/6. Quiz Solution Discussion.mp4 5.9 MB
26. Word Embeddings/3. Measuring Word Embeddings.mp4 5.5 MB
8. Introduction to PyTorch/1. CODE FOR THIS COURSE.mp4 1.8 MB
19. Convolutional Networks Visualization/dog.jpg 93.3 KB
21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap-en_US.srt 42.5 KB
21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE-en_US.srt 37.4 KB
8. Introduction to PyTorch/9. Loss Functions in PyTorch-en_US.srt 36.9 KB
19. Convolutional Networks Visualization/imagenet-class-index.json 34.5 KB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network-en_US.srt 32.6 KB
14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN-en_US.srt 31.8 KB
30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder-en_US.srt 31.0 KB
25. Recurrent Neural Networks/7. LSTMs-en_US.srt 28.4 KB
11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation-en_US.srt 27.6 KB
22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1-en_US.srt 25.5 KB
33. Build a Chatbot with Transformers/16. Loss with Label Smoothing-en_US.srt 24.8 KB
8. Introduction to PyTorch/4. How PyTorch Works-en_US.srt 24.0 KB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network-en_US.srt 23.2 KB
15. CNN Architectures/3. Residual Networks Part 2-en_US.srt 23.0 KB
31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder-en_US.srt 22.9 KB
30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2-en_US.srt 22.6 KB
31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1-en_US.srt 22.5 KB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network-en_US.srt 22.4 KB
31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function-en_US.srt 21.7 KB
1. How Neural Networks and Backpropagation Works/4. The Perceptron-en_US.srt 21.2 KB
11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function-en_US.srt 21.0 KB
14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN-en_US.srt 20.8 KB
35. Google Colab and Gradient Accumulation/2. Gradient Accumulation-en_US.srt 20.7 KB
33. Build a Chatbot with Transformers/19. Evaluation Function-en_US.srt 20.6 KB
31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function-en_US.srt 20.5 KB
30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model-en_US.srt 20.4 KB
36. BERT/5. Exploring Transformers-en_US.srt 20.1 KB
33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2-en_US.srt 19.9 KB
28. Saving and Loading Models/1. Saving and Loading Part 1-en_US.srt 19.3 KB
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