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

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

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文件列表

  • 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
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  • 23. Neural Style Transfer/2. NST Theory Part 2-en_US.srt 8.1 kB
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  • 26. Word Embeddings/5. Word Embeddings in PyTorch-en_US.srt 8.0 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction-en_US.srt 8.0 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder-en_US.srt 7.9 kB
  • 32. Transformers/13. KL Divergence Loss-en_US.srt 7.9 kB
  • 5. Optimization/7. Momentum-en_US.srt 7.8 kB
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  • 27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary-en_US.srt 7.7 kB
  • 28. Saving and Loading Models/3. Saving and Loading Part 3-en_US.srt 7.7 kB
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  • 20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11-en_US.srt 7.6 kB
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  • 36. BERT/2. Masked Language Modelling-en_US.srt 7.3 kB
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