Udemy - The Complete Neural Networks Bootcamp Theory, Applications

磁力链接/BT种子名称

Udemy - The Complete Neural Networks Bootcamp Theory, Applications

磁力链接/BT种子简介

种子哈希：97a5f734d6035bf59616f2b609299b572f1605ab
文件大小： 18.79G
已经下载：1296次
下载速度：极快
收录时间：2024-04-18
最近下载：2025-01-01

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:97A5F734D6035BF59616F2B609299B572F1605AB
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 暗网禁地 91短视频 TikTok成人版 PornHub 草榴社区 乱伦社区 少女初夜 萝莉岛

最近搜索

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
  • 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
  • 5. Optimization/3. Mini-Batch Gradient Descent.mp4 7.3 MB
  • 13. Convolutional Neural Networks/6. Quiz Solution Discussion.mp4 6.2 MB
  • 26. Word Embeddings/3. Measuring Word Embeddings.mp4 5.8 MB
  • 8. Introduction to PyTorch/1. CODE FOR THIS COURSE.mp4 1.9 MB
  • 19. Convolutional Networks Visualization/dog.jpg 95.5 kB
  • 21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap-en_US.srt 43.5 kB
  • 21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE-en_US.srt 38.2 kB
  • 8. Introduction to PyTorch/9. Loss Functions in PyTorch-en_US.srt 37.7 kB
  • 19. Convolutional Networks Visualization/imagenet-class-index.json 35.4 kB
  • 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network-en_US.srt 33.3 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN-en_US.srt 32.6 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder-en_US.srt 31.7 kB
  • 25. Recurrent Neural Networks/7. LSTMs-en_US.srt 29.1 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation-en_US.srt 28.2 kB
  • 22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1-en_US.srt 26.1 kB
  • 33. Build a Chatbot with Transformers/16. Loss with Label Smoothing-en_US.srt 25.4 kB
  • 8. Introduction to PyTorch/4. How PyTorch Works-en_US.srt 24.6 kB
  • 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network-en_US.srt 23.7 kB
  • 15. CNN Architectures/3. Residual Networks Part 2-en_US.srt 23.6 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder-en_US.srt 23.4 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2-en_US.srt 23.1 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1-en_US.srt 23.0 kB
  • 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network-en_US.srt 23.0 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function-en_US.srt 22.2 kB
  • 1. How Neural Networks and Backpropagation Works/4. The Perceptron-en_US.srt 21.7 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function-en_US.srt 21.6 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN-en_US.srt 21.3 kB
  • 35. Google Colab and Gradient Accumulation/2. Gradient Accumulation-en_US.srt 21.2 kB
  • 33. Build a Chatbot with Transformers/19. Evaluation Function-en_US.srt 21.1 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function-en_US.srt 21.0 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model-en_US.srt 20.9 kB
  • 36. BERT/5. Exploring Transformers-en_US.srt 20.6 kB
  • 33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2-en_US.srt 20.4 kB
  • 28. Saving and Loading Models/1. Saving and Loading Part 1-en_US.srt 19.7 kB
  • 33. Build a Chatbot with Transformers/7. Embeddings-en_US.srt 19.2 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters-en_US.srt 19.0 kB
  • 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing-en_US.srt 19.0 kB
  • 24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4-en_US.srt 18.9 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1-en_US.srt 18.7 kB
  • 1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do-en_US.srt 18.6 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions-en_US.srt 18.6 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1-en_US.srt 18.5 kB
  • 32. Transformers/3. Positional Encoding-en_US.srt 18.5 kB
  • 15. CNN Architectures/5. Densely Connected Networks-en_US.srt 18.3 kB
  • 19. Convolutional Networks Visualization/2. Processing the Model-en_US.srt 18.0 kB
  • 8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks-en_US.srt 17.8 kB
  • 34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code-en_US.srt 17.7 kB
  • 29. Sequence Modelling/1. Sequence Modeling-en_US.srt 17.7 kB
  • 2. Loss Functions/4. Binary Cross Entropy Loss-en_US.srt 17.6 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3-en_US.srt 17.5 kB
  • 37. Vision Transformers/1. Vision Transformer Part 1-en_US.srt 17.4 kB
  • 16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4-en_US.srt 17.4 kB
  • 13. Convolutional Neural Networks/8. Activation, Pooling and FC-en_US.srt 17.3 kB
  • 33. Build a Chatbot with Transformers/6. Data Loading and Masking-en_US.srt 17.2 kB
  • 2. Loss Functions/9. Hinge Loss-en_US.srt 16.9 kB
  • 25. Recurrent Neural Networks/4. Backpropagation Through Time-en_US.srt 16.9 kB
  • 2. Loss Functions/10. Triplet Ranking Loss-en_US.srt 16.8 kB
  • 8. Introduction to PyTorch/10. Weight Initialization in PyTorch-en_US.srt 16.8 kB
  • 19. Convolutional Networks Visualization/3. Visualizing the Feature Maps-en_US.srt 16.8 kB
  • 6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay-en_US.srt 16.8 kB
  • 27. Practical Recurrent Networks in PyTorch/6. Generating Text-en_US.srt 16.7 kB
  • 32. Transformers/12. Cross Entropy Loss-en_US.srt 16.5 kB
  • 20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2-en_US.srt 16.5 kB
  • 33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3-en_US.srt 16.5 kB
  • 16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2-en_US.srt 16.4 kB
  • 2. Loss Functions/8. Contrastive Loss-en_US.srt 16.3 kB
  • 16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1-en_US.srt 16.3 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset-en_US.srt 16.3 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations-en_US.srt 16.3 kB
  • 4. Regularization and Normalization/6. Batch Normalization-en_US.srt 16.3 kB
  • 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters-en_US.srt 16.3 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details-en_US.srt 16.2 kB
  • 32. Transformers/1. Introduction to Transformers-en_US.srt 16.2 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters-en_US.srt 16.0 kB
  • 16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3-en_US.srt 15.9 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2-en_US.srt 15.9 kB
  • 18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation-en_US.srt 15.8 kB
  • 37. Vision Transformers/3. Vision Transformer Part 3-en_US.srt 15.8 kB
  • 13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs-en_US.srt 15.8 kB
  • 22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3-en_US.srt 15.7 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation-en_US.srt 15.7 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images-en_US.srt 15.7 kB
  • 1. How Neural Networks and Backpropagation Works/5. Gradient Descent-en_US.srt 15.6 kB
  • 15. CNN Architectures/1. CNN Architectures Part 1-en_US.srt 15.6 kB
  • 5. Optimization/8. RMSProp-en_US.srt 15.5 kB
  • 8. Introduction to PyTorch/5. Torch Tensors - Part 1-en_US.srt 15.4 kB
  • 15. CNN Architectures/7. Seperable Convolutions-en_US.srt 15.2 kB
  • 8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions-en_US.srt 15.2 kB
  • 33. Build a Chatbot with Transformers/14. Transformer-en_US.srt 15.1 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2-en_US.srt 15.1 kB
  • 22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2-en_US.srt 15.0 kB
  • 29. Sequence Modelling/4. How Attention Mechanisms Work-en_US.srt 15.0 kB
  • 18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data-en_US.srt 14.9 kB
  • 24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3-en_US.srt 14.9 kB
  • 8. Introduction to PyTorch/3. Installing PyTorch and an Introduction-en_US.srt 14.6 kB
  • 1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1-en_US.srt 14.5 kB
  • 27. Practical Recurrent Networks in PyTorch/4. Creating the Network-en_US.srt 14.5 kB
  • 10. Visualize the Learning Process/5. Visualize Learning Part 5-en_US.srt 14.5 kB
  • 15. CNN Architectures/2. Residual Networks Part 1-en_US.srt 14.5 kB
  • 18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset-en_US.srt 14.4 kB
  • 24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1-en_US.srt 14.3 kB
  • 33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3-en_US.srt 14.2 kB
  • 33. Build a Chatbot with Transformers/18. Training Function-en_US.srt 14.2 kB
  • 21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders-en_US.srt 14.2 kB
  • 1. How Neural Networks and Backpropagation Works/6. The Forward Propagation-en_US.srt 14.1 kB
  • 31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation-en_US.srt 14.0 kB
  • 38. GPT/1. GPT Part 1-en_US.srt 14.0 kB
  • 25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem-en_US.srt 13.9 kB
  • 23. Neural Style Transfer/3. NST Theory Part 3-en_US.srt 13.8 kB
  • 27. Practical Recurrent Networks in PyTorch/2. Processing the Text-en_US.srt 13.7 kB
  • 20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12-en_US.srt 13.6 kB
  • 27. Practical Recurrent Networks in PyTorch/5. Training the Network-en_US.srt 13.6 kB
  • 33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1-en_US.srt 13.5 kB
  • 15. CNN Architectures/6. Squeeze-Excite Networks-en_US.srt 13.5 kB
  • 8. Introduction to PyTorch/6. Torch Tensors - Part 2-en_US.srt 13.4 kB
  • 32. Transformers/4. MultiHead Attention Part 1-en_US.srt 13.3 kB
  • 6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate-en_US.srt 13.3 kB
  • 18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results-en_US.srt 13.3 kB
  • 1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks-en_US.srt 13.1 kB
  • 7. Weight Initialization/2. What happens when all weights are initialized to the same value-en_US.srt 13.0 kB
  • 7. Weight Initialization/3. Xavier Initialization-en_US.srt 12.9 kB
  • 13. Convolutional Neural Networks/14. Softmax with Temperature-en_US.srt 12.9 kB
  • 33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5-en_US.srt 12.9 kB
  • 24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2-en_US.srt 12.8 kB
  • 33. Build a Chatbot with Transformers/20. Main Function and User Evaluation-en_US.srt 12.8 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset-en_US.srt 12.7 kB
  • 20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3-en_US.srt 12.6 kB
  • 13. Convolutional Neural Networks/3. Filters and Features-en_US.srt 12.5 kB
  • 26. Word Embeddings/1. What are Word Embeddings-en_US.srt 12.5 kB
  • 38. GPT/2. GPT Part 2-en_US.srt 12.5 kB
  • 20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6-en_US.srt 12.5 kB
  • 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class-en_US.srt 12.3 kB
  • 10. Visualize the Learning Process/1. Visualize Learning Part 1-en_US.srt 12.3 kB
  • 1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2-en_US.srt 12.3 kB
  • 4. Regularization and Normalization/3. Dropout-en_US.srt 12.3 kB
  • 32. Transformers/15. Dropout-en_US.srt 12.3 kB
  • 8. Introduction to PyTorch/8. Automatic Differentiation-en_US.srt 12.2 kB
  • 37. Vision Transformers/2. Vision Transformer Part 2-en_US.srt 12.1 kB
  • 4. Regularization and Normalization/2. L1 and L2 Regularization-en_US.srt 12.1 kB
  • 21. Autoencoders and Variational Autoencoders/1. Autoencoders-en_US.srt 12.1 kB
  • 5. Optimization/13. AMSGrad-en_US.srt 11.9 kB
  • 15. CNN Architectures/8. Transfer Learning-en_US.srt 11.8 kB
  • 36. BERT/3. Next Sentence Prediction-en_US.srt 11.8 kB
  • 5. Optimization/5. Exponentially Weighted Average Implementation-en_US.srt 11.6 kB
  • 36. BERT/1. What is BERT and its structure-en_US.srt 11.5 kB
  • 17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication-en_US.srt 11.4 kB
  • 34. Universal Transformers/3. Transformers for other tasks-en_US.srt 11.4 kB
  • 2. Loss Functions/2. L1 Loss (MAE)-en_US.srt 11.2 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation-en_US.srt 11.2 kB
  • 25. Recurrent Neural Networks/2. Vanilla RNNs-en_US.srt 11.1 kB
  • 18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network-en_US.srt 11.0 kB
  • 13. Convolutional Neural Networks/11. CNN Characteristics-en_US.srt 11.0 kB
  • 2. Loss Functions/5. Cross Entropy Loss-en_US.srt 11.0 kB
  • 38. GPT/4. Byte-Pair Encoding-en_US.srt 10.7 kB
  • 32. Transformers/5. MultiHead Attention Part 2-en_US.srt 10.7 kB
  • 35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab-en_US.srt 10.7 kB
  • 38. GPT/3. Zero-Shot Predictions with GPT-en_US.srt 10.6 kB
  • 33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2-en_US.srt 10.6 kB
  • 10. Visualize the Learning Process/3. Visualize Learning Part 3-en_US.srt 10.6 kB
  • 20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5-en_US.srt 10.5 kB
  • 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization-en_US.srt 10.5 kB
  • 19. Convolutional Networks Visualization/1. Data and the Model-en_US.srt 10.3 kB
  • 4. Regularization and Normalization/7. Layer Normalization-en_US.srt 10.3 kB
  • 21. Autoencoders and Variational Autoencoders/7. Deep Fake-en_US.srt 10.3 kB
  • 28. Saving and Loading Models/2. Saving and Loading Part 2-en_US.srt 10.3 kB
  • 10. Visualize the Learning Process/6. Visualize Learning Part 6-en_US.srt 10.2 kB
  • 2. Loss Functions/6. Softmax Function-en_US.srt 10.1 kB
  • 33. Build a Chatbot with Transformers/12. Encoder Layer-en_US.srt 10.1 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence-en_US.srt 10.0 kB
  • 38. GPT/6. Playing with HuggingFace models-en_US.srt 10.0 kB
  • 2. Loss Functions/7. KL divergence Loss-en_US.srt 9.8 kB
  • 27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters-en_US.srt 9.8 kB
  • 9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset-en_US.srt 9.7 kB
  • 32. Transformers/8. Layer Normalization-en_US.srt 9.6 kB
  • 5. Optimization/9. Adam Optimization-en_US.srt 9.5 kB
  • 34. Universal Transformers/1. Universal Transformers-en_US.srt 9.5 kB
  • 5. Optimization/11. Weight Decay-en_US.srt 9.5 kB
  • 21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders-en_US.srt 9.5 kB
  • 13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them-en_US.srt 9.4 kB
  • 2. Loss Functions/1. Mean Squared Error (MSE)-en_US.srt 9.4 kB
  • 23. Neural Style Transfer/1. NST Theory Part 1-en_US.srt 9.4 kB
  • 3. Activation Functions/4. ReLU and PReLU-en_US.srt 9.4 kB
  • 36. BERT/4. Fine-tuning BERT-en_US.srt 9.4 kB
  • 38. GPT/5. Technical Details of GPT-en_US.srt 9.2 kB
  • 17. Transposed Convolutions/1. Introduction to Transposed Convolutions-en_US.srt 9.2 kB
  • 25. Recurrent Neural Networks/9. GRUs-en_US.srt 9.1 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN-en_US.srt 9.1 kB
  • 20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7-en_US.srt 9.0 kB
  • 33. Build a Chatbot with Transformers/15. AdamWarmup-en_US.srt 9.0 kB
  • 33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1-en_US.srt 8.9 kB
  • 32. Transformers/10. Masked MultiHead Attention-en_US.srt 8.9 kB
  • 13. Convolutional Neural Networks/5. More on Convolutions-en_US.srt 8.9 kB
  • 20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4-en_US.srt 8.9 kB
  • 7. Weight Initialization/1. Normal Distribution-en_US.srt 8.8 kB
  • 32. Transformers/16. Learning Rate Warmup-en_US.srt 8.8 kB
  • 32. Transformers/2. Input Embeddings-en_US.srt 8.7 kB
  • 32. Transformers/7. Residual Learning-en_US.srt 8.6 kB
  • 33. Build a Chatbot with Transformers/17. Defining the Model-en_US.srt 8.6 kB
  • 17. Transposed Convolutions/3. Transposed Convolutions-en_US.srt 8.5 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network-en_US.srt 8.5 kB
  • 5. Optimization/1. Batch Gradient Descent-en_US.srt 8.5 kB
  • 3. Activation Functions/2. Sigmoid Activation-en_US.srt 8.4 kB
  • 2. Loss Functions/3. Huber Loss-en_US.srt 8.4 kB
  • 1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning-en_US.srt 8.3 kB
  • 5. Optimization/6. Bias Correction in Exponentially Weighted Averages-en_US.srt 8.1 kB
  • 23. Neural Style Transfer/2. NST Theory Part 2-en_US.srt 8.1 kB
  • 4. Regularization and Normalization/8. Group Normalization-en_US.srt 8.0 kB
  • 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
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation-en_US.srt 7.8 kB
  • 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
  • 3. Activation Functions/8. Mish Activation-en_US.srt 7.7 kB
  • 20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11-en_US.srt 7.6 kB
  • 6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts-en_US.srt 7.3 kB
  • 36. BERT/2. Masked Language Modelling-en_US.srt 7.3 kB
  • 20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8-en_US.srt 7.2 kB
  • 10. Visualize the Learning Process/4. Visualize Learning Part 4-en_US.srt 7.2 kB
  • 29. Sequence Modelling/3. Attention Mechanisms-en_US.srt 7.2 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/5. Prediction-en_US.srt 7.1 kB
  • 5. Optimization/4. Exponentially Weighted Average Intuition-en_US.srt 7.0 kB
  • 13. Convolutional Neural Networks/10. Important formulas-en_US.srt 7.0 kB
  • 18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network-en_US.srt 7.0 kB
  • 10. Visualize the Learning Process/7. Neural Networks Playground-en_US.srt 6.9 kB
  • 33. Build a Chatbot with Transformers/13. Decoder Layer-en_US.srt 6.9 kB
  • 20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1-en_US.srt 6.9 kB
  • 25. Recurrent Neural Networks/1. Why do we need RNNs-en_US.srt 6.8 kB
  • 29. Sequence Modelling/2. Image Captioning-en_US.srt 6.8 kB
  • 6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap-en_US.srt 6.8 kB
  • 5. Optimization/2. Stochastic Gradient Descent-en_US.srt 6.7 kB
  • 30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing-en_US.srt 6.6 kB
  • 21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders-en_US.srt 6.6 kB
  • 4. Regularization and Normalization/1. Overfitting-en_US.srt 6.6 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action-en_US.srt 6.5 kB
  • 25. Recurrent Neural Networks/10. CNN-LSTM-en_US.srt 6.5 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image-en_US.srt 6.5 kB
  • 13. Convolutional Neural Networks/1. Prerequisite Filters-en_US.srt 6.5 kB
  • 4. Regularization and Normalization/5. Normalization-en_US.srt 6.2 kB
  • 13. Convolutional Neural Networks/7. A Tool for Convolution Visualization-en_US.srt 6.1 kB
  • 32. Transformers/14. Label Smoothing-en_US.srt 6.1 kB
  • 5. Optimization/12. Decoupling Weight Decay-en_US.srt 5.9 kB
  • 12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network-en_US.srt 5.8 kB
  • 33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4-en_US.srt 5.8 kB
  • 14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model-en_US.srt 5.7 kB
  • 11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model-en_US.srt 5.4 kB
  • 20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9-en_US.srt 5.4 kB
  • 25. Recurrent Neural Networks/8. Bidirectional RNNs-en_US.srt 5.3 kB
  • 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 1.4 kB
  • 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
  • [Tutorialsplanet.NET].url 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 0 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！
>