Udemy - The Complete Neural Networks Bootcamp Theory, Applications (11.2021)

磁力链接/BT种子名称

Udemy - The Complete Neural Networks Bootcamp Theory, Applications (11.2021)

磁力链接/BT种子简介

种子哈希：15b4d062a18983c064a36b9a8f7ac3a7c59709ba
文件大小： 13.38G
已经下载：391次
下载速度：极快
收录时间：2025-07-18
最近下载：2025-12-13

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:15B4D062A18983C064A36B9A8F7AC3A7C59709BA
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 世界之窗 小蓝俱乐部 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 母狗园 51动漫 91短视频 抖音Max 海王TV TikTok成人版 PornHub 暗网Xvideo 草榴社区 哆哔涩漫 呦乐园 萝莉岛 搜同 91暗网

最近搜索

文件列表

  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/004 Defining the Encoder.mp4 233.4 MB
  • 22 - Autoencoders and Variational Autoencoders/006 Loss Function Derivation for VAE.mp4 229.9 MB
  • 22 - Autoencoders and Variational Autoencoders/005 Probability Distributions Recap.mp4 196.8 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/006 Training the Network.mp4 191.6 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/003 Building the CNN.mp4 179.7 MB
  • 01 - How Neural Networks and Backpropagation Works/002 What Can Deep Learning Do.mp4 163.8 MB
  • 08 - Introduction to PyTorch/009 Loss Functions in PyTorch.mp4 162.5 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/006 Designing the Attention Model.mp4 154.6 MB
  • 34 - Build a Chatbot with Transformers/017 Loss with Label Smoothing.mp4 145.3 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/001 Data Augmentation.mp4 126.2 MB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/005 Part 4 Building the Network.mp4 114.9 MB
  • 16 - CNN Architectures/003 Residual Networks Part 2.mp4 112.9 MB
  • 38 - Vision Transformers/003 Vision Transformer Part 3.mp4 111.6 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/010 Train Function.mp4 111.0 MB
  • 35 - Universal Transformers/002 Practical Universal Transformers Modifying the Transformers code.mp4 110.2 MB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/006 Part 5 Training the Network.mp4 109.8 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/008 Designing the Decoder Part 2.mp4 109.4 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/008 Backpropagation.mp4 107.0 MB
  • 16 - CNN Architectures/005 Stochastic Depth.mp4 105.8 MB
  • 28 - Practical Recurrent Networks in PyTorch/007 Generating Text.mp4 103.1 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/002 Loading the Dataset.mp4 101.3 MB
  • 39 - GPT/013 (6) GPT Implementation Part 1.mp4 101.3 MB
  • 39 - GPT/012 (5) GPT Implementation Part 1.mp4 100.4 MB
  • 09 - Data Augmentation/003 2_Data Augmentation Techniques Part 2.mp4 99.8 MB
  • 17 - Practical Residual Networks in PyTorch/004 Practical ResNet Part 4.mp4 97.6 MB
  • 34 - Build a Chatbot with Transformers/003 Dataset Preprocessing Part 2.mp4 94.4 MB
  • 08 - Introduction to PyTorch/004 How PyTorch Works.mp4 93.1 MB
  • 25 - Practical Neural Style Transfer in PyTorch/004 NST Practical Part 4.mp4 92.3 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/004 Constructing the Dataset Part 1.mp4 91.9 MB
  • 37 - BERT/005 Exploring Transformers.mp4 91.3 MB
  • 16 - CNN Architectures/002 Residual Networks Part 1.mp4 91.0 MB
  • 02 - Loss Functions/011 Triplet Ranking Loss.mp4 90.9 MB
  • 38 - Vision Transformers/001 Vision Transformer Part 1.mp4 89.4 MB
  • 26 - Recurrent Neural Networks/007 LSTMs.mp4 89.0 MB
  • 25 - Practical Neural Style Transfer in PyTorch/002 NST Practical Part 2.mp4 88.7 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/007 Designing the Decoder Part 1.mp4 88.5 MB
  • 21 - YOLO Object Detection (Theory)/003 YOLO Theory Part 3.mp4 88.5 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/009 Creating the Decoder Part 3.mp4 88.1 MB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/002 Part 1 Data Preprocessing.mp4 86.6 MB
  • 28 - Practical Recurrent Networks in PyTorch/006 Training the Network.mp4 86.5 MB
  • 34 - Build a Chatbot with Transformers/011 MultiHead Attention Implementation Part 3.mp4 86.4 MB
  • 30 - Sequence Modelling/001 Sequence Modeling.mp4 85.5 MB
  • 21 - YOLO Object Detection (Theory)/006 YOLO Theory Part 6.mp4 85.3 MB
  • 09 - Data Augmentation/002 2_Data Augmentation Techniques Part 1.mp4 85.2 MB
  • 20 - Convolutional Networks Visualization/002 Processing the Model.mp4 84.4 MB
  • 01 - How Neural Networks and Backpropagation Works/005 The Perceptron.mp4 84.3 MB
  • 39 - GPT/010 (3) GPT Implementation Part 1.mp4 84.1 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/003 Importing and Defining Parameters.mp4 82.9 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/007 Creating the Decoder Part 1.mp4 82.8 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/006 Training the CNN.mp4 82.0 MB
  • 39 - GPT/009 (2) GPT Implementation Part 1.mp4 81.0 MB
  • 34 - Build a Chatbot with Transformers/015 Transformer.mp4 80.7 MB
  • 09 - Data Augmentation/004 2_Data Augmentation Techniques Part 3.mp4 80.6 MB
  • 20 - Convolutional Networks Visualization/003 Visualizing the Feature Maps.mp4 80.5 MB
  • 29 - Saving and Loading Models/001 Saving and Loading Part 1.mp4 79.9 MB
  • 35 - Universal Transformers/003 Transformers for other tasks.mp4 79.4 MB
  • 34 - Build a Chatbot with Transformers/020 Evaluation Function.mp4 77.6 MB
  • 21 - YOLO Object Detection (Theory)/001 YOLO Theory Part 1.mp4 75.4 MB
  • 23 - Practical Variational Autoencoders in PyTorch/001 Practical VAE Part 1.mp4 74.9 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/011 Defining Hyperparameters.mp4 73.7 MB
  • 21 - YOLO Object Detection (Theory)/005 YOLO Theory Part 5.mp4 73.5 MB
  • 25 - Practical Neural Style Transfer in PyTorch/003 NST Practical Part 3.mp4 73.3 MB
  • 33 - Transformers/004 Positional Encoding.mp4 72.9 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/007 Backpropagation Equations.mp4 72.7 MB
  • 04 - Regularization and Normalization/006 Batch Normalization.mp4 71.9 MB
  • 17 - Practical Residual Networks in PyTorch/003 Practical ResNet Part 3.mp4 71.7 MB
  • 08 - Introduction to PyTorch/006 Torch Tensors - Part 2.mp4 71.2 MB
  • 34 - Build a Chatbot with Transformers/019 Training Function.mp4 70.4 MB
  • 23 - Practical Variational Autoencoders in PyTorch/002 Practical VAE Part 2.mp4 70.0 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/006 Testing and Visualizing the results.mp4 70.0 MB
  • 16 - CNN Architectures/007 Densely Connected Networks.mp4 69.1 MB
  • 14 - Convolutional Neural Networks/014 DropBlock Dropout in CNNs.mp4 68.5 MB
  • 07 - Weight Initialization/003 Xavier Initialization.mp4 68.1 MB
  • 28 - Practical Recurrent Networks in PyTorch/005 Creating the Network.mp4 67.6 MB
  • 23 - Practical Variational Autoencoders in PyTorch/003 Practical VAE Part 3.mp4 66.6 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/008 Creating the Decoder Part 2.mp4 66.2 MB
  • 39 - GPT/001 GPT Part 1.mp4 66.2 MB
  • 34 - Build a Chatbot with Transformers/021 Main Function and User Evaluation.mp4 65.9 MB
  • 28 - Practical Recurrent Networks in PyTorch/003 Processing the Text.mp4 65.8 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/012 Evaluation Function.mp4 64.7 MB
  • 34 - Build a Chatbot with Transformers/006 Dataset Preprocessing Part 5.mp4 64.1 MB
  • 05 - Optimization/013 AMSGrad.mp4 63.9 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/003 Forward Propagation.mp4 63.3 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/006 Creating the Encoder.mp4 62.9 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/004 Understanding the data.mp4 61.7 MB
  • 14 - Convolutional Neural Networks/009 Activation, Pooling and FC.mp4 61.6 MB
  • 17 - Practical Residual Networks in PyTorch/002 Practical ResNet Part 2.mp4 61.3 MB
  • 39 - GPT/014 (7) GPT Implementation Part 1.mp4 59.5 MB
  • 22 - Autoencoders and Variational Autoencoders/007 Deep Fake.mp4 59.3 MB
  • 34 - Build a Chatbot with Transformers/013 Encoder Layer.mp4 58.8 MB
  • 29 - Saving and Loading Models/002 Saving and Loading Part 2.mp4 58.1 MB
  • 34 - Build a Chatbot with Transformers/002 Dataset Preprocessing Part 1.mp4 57.9 MB
  • 34 - Build a Chatbot with Transformers/004 Dataset Preprocessing Part 3.mp4 57.8 MB
  • 08 - Introduction to PyTorch/003 Installing PyTorch and an Introduction.mp4 57.5 MB
  • 05 - Optimization/011 Weight Decay.mp4 56.9 MB
  • 34 - Build a Chatbot with Transformers/007 Data Loading and Masking.mp4 56.6 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/003 Modifying the Network.mp4 56.5 MB
  • 02 - Loss Functions/002 L1 Loss (MAE).mp4 56.3 MB
  • 34 - Build a Chatbot with Transformers/008 Embeddings.mp4 55.7 MB
  • 05 - Optimization/009 Adam Optimization.mp4 55.5 MB
  • 21 - YOLO Object Detection (Theory)/008 YOLO Theory Part 8.mp4 54.6 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/003 Understanding the Encoder.mp4 54.6 MB
  • 21 - YOLO Object Detection (Theory)/002 YOLO Theory Part 2.mp4 54.1 MB
  • 08 - Introduction to PyTorch/005 Torch Tensors - Part 1.mp4 53.8 MB
  • 04 - Regularization and Normalization/003 Dropout.mp4 53.7 MB
  • 33 - Transformers/016 Dropout.mp4 53.7 MB
  • 18 - Transposed Convolutions/002 Convolution Operation as Matrix Multiplication.mp4 53.4 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/003 Accuracy Calculation.mp4 53.3 MB
  • 11 - Visualize the Learning Process/005 Visualize Learning Part 5.mp4 53.2 MB
  • 17 - Practical Residual Networks in PyTorch/001 Practical ResNet Part 1.mp4 53.2 MB
  • 22 - Autoencoders and Variational Autoencoders/004 Variational Autoencoders.mp4 53.0 MB
  • 01 - How Neural Networks and Backpropagation Works/004 The Essence of Neural Networks.mp4 52.4 MB
  • 24 - Neural Style Transfer/003 NST Theory Part 3.mp4 52.2 MB
  • 34 - Build a Chatbot with Transformers/016 AdamWarmup.mp4 51.8 MB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/003 Cyclic Learning Rate.mp4 51.7 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/004 Loss Function.mp4 50.9 MB
  • 02 - Loss Functions/010 Hinge Loss.mp4 50.9 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/001 The Dataset and Hyperparameters.mp4 50.9 MB
  • 21 - YOLO Object Detection (Theory)/007 YOLO Theory Part 7.mp4 50.7 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/004 Defining the Network Class.mp4 50.5 MB
  • 26 - Recurrent Neural Networks/006 Vanishing and Exploding Gradient Problem.mp4 49.5 MB
  • 33 - Transformers/003 Input Embeddings.mp4 48.7 MB
  • 08 - Introduction to PyTorch/007 Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4 47.6 MB
  • 25 - Practical Neural Style Transfer in PyTorch/001 NST Practical Part 1.mp4 47.4 MB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/002 Step Learning Rate Decay.mp4 47.4 MB
  • 08 - Introduction to PyTorch/008 Automatic Differentiation.mp4 47.0 MB
  • 16 - CNN Architectures/009 Seperable Convolutions.mp4 46.8 MB
  • 27 - Word Embeddings/001 What are Word Embeddings.mp4 46.4 MB
  • 08 - Introduction to PyTorch/010 Weight Initialization in PyTorch.mp4 46.3 MB
  • 07 - Weight Initialization/002 What happens when all weights are initialized to the same value.mp4 46.0 MB
  • 33 - Transformers/005 MultiHead Attention Part 1.mp4 45.8 MB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/004 Part 3 Creating and Loading the Dataset.mp4 45.4 MB
  • 16 - CNN Architectures/011 Is a 1x1 convolutional filter equivalent to a FC layer.mp4 45.3 MB
  • 02 - Loss Functions/009 Contrastive Loss.mp4 44.9 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/002 Visualizing and Loading the Dataset.mp4 44.3 MB
  • 20 - Convolutional Networks Visualization/001 Data and the Model.mp4 44.0 MB
  • 34 - Build a Chatbot with Transformers/014 Decoder Layer.mp4 43.9 MB
  • 01 - How Neural Networks and Backpropagation Works/003 The Rise of Deep Learning.mp4 43.8 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/002 Introduction.mp4 43.6 MB
  • 08 - Introduction to PyTorch/002 Computation Graphs and Deep Learning Frameworks.mp4 43.5 MB
  • 34 - Build a Chatbot with Transformers/009 MultiHead Attention Implementation Part 1.mp4 43.1 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/005 Constructing the Dataset Part 2.mp4 43.0 MB
  • 36 - Google Colab and Gradient Accumulation/002 Gradient Accumulation.mp4 42.6 MB
  • 28 - Practical Recurrent Networks in PyTorch/004 Defining and Visualizing the Parameters.mp4 42.5 MB
  • 01 - How Neural Networks and Backpropagation Works/007 The Forward Propagation.mp4 42.4 MB
  • 26 - Recurrent Neural Networks/004 Backpropagation Through Time.mp4 41.6 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/009 Initializing the Network.mp4 41.3 MB
  • 11 - Visualize the Learning Process/006 Visualize Learning Part 6.mp4 41.2 MB
  • 21 - YOLO Object Detection (Theory)/012 YOLO Theory Part 12.mp4 41.0 MB
  • 26 - Recurrent Neural Networks/002 Vanilla RNNs.mp4 40.0 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/001 Implementation Details.mp4 39.8 MB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/003 Part 2 Data Normalization.mp4 39.5 MB
  • 39 - GPT/005 Technical Details of GPT.mp4 39.3 MB
  • 21 - YOLO Object Detection (Theory)/011 YOLO Theory Part 11.mp4 38.8 MB
  • 14 - Convolutional Neural Networks/003 Filters and Features.mp4 38.4 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/001 Loading and Normalizing the Dataset.mp4 38.4 MB
  • 14 - Convolutional Neural Networks/001 Prerequisite Filters.mp4 38.2 MB
  • 24 - Neural Style Transfer/001 NST Theory Part 1.mp4 37.7 MB
  • 37 - BERT/004 Fine-tuning BERT.mp4 37.7 MB
  • 38 - Vision Transformers/002 Vision Transformer Part 2.mp4 37.0 MB
  • 05 - Optimization/001 Batch Gradient Descent.mp4 37.0 MB
  • 33 - Transformers/002 Introduction to Transformers.mp4 36.7 MB
  • 05 - Optimization/012 Decoupling Weight Decay.mp4 36.7 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/010 Classifying your own Handwritten images.mp4 36.7 MB
  • 34 - Build a Chatbot with Transformers/010 MultiHead Attention Implementation Part 2.mp4 36.6 MB
  • 33 - Transformers/006 MultiHead Attention Part 2.mp4 36.6 MB
  • 28 - Practical Recurrent Networks in PyTorch/002 Creating the Dictionary.mp4 36.3 MB
  • 29 - Saving and Loading Models/003 Saving and Loading Part 3.mp4 35.9 MB
  • 39 - GPT/003 Zero-Shot Predictions with GPT.mp4 35.1 MB
  • 14 - Convolutional Neural Networks/012 CNN Characteristics.mp4 34.9 MB
  • 04 - Regularization and Normalization/007 Layer Normalization.mp4 34.8 MB
  • 02 - Loss Functions/006 Softmax Function.mp4 34.2 MB
  • 39 - GPT/002 GPT Part 2.mp4 34.0 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/005 Creating the network class and the network functions.mp4 33.9 MB
  • 27 - Word Embeddings/005 Word Embeddings in PyTorch.mp4 32.7 MB
  • 16 - CNN Architectures/001 CNN Architectures Part 1.mp4 32.3 MB
  • 22 - Autoencoders and Variational Autoencoders/001 Autoencoders.mp4 32.0 MB
  • 09 - Data Augmentation/001 1_Introduction to Data Augmentation.mp4 31.8 MB
  • 02 - Loss Functions/004 Binary Cross Entropy Loss.mp4 31.4 MB
  • 19 - Transfer Learning in PyTorch - Image Classification/005 Finetuning the Network.mp4 31.0 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/002 Utility Functions.mp4 30.7 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/008 Plotting and Putting into Action.mp4 30.4 MB
  • 34 - Build a Chatbot with Transformers/018 Defining the Model.mp4 30.4 MB
  • 25 - Practical Neural Style Transfer in PyTorch/005 Fast Neural Style Transfer.mp4 30.3 MB
  • 30 - Sequence Modelling/004 How Attention Mechanisms Work.mp4 29.4 MB
  • 03 - Activation Functions/008 Mish Activation.mp4 29.3 MB
  • 01 - How Neural Networks and Backpropagation Works/006 Gradient Descent.mp4 29.0 MB
  • 37 - BERT/003 Next Sentence Prediction.mp4 29.0 MB
  • 34 - Build a Chatbot with Transformers/012 Feed Forward Implementation.mp4 28.9 MB
  • 05 - Optimization/005 Exponentially Weighted Average Implementation.mp4 28.9 MB
  • 16 - CNN Architectures/008 Squeeze-Excite Networks.mp4 28.8 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/010 Training the Model.mp4 28.6 MB
  • 18 - Transposed Convolutions/003 Transposed Convolutions.mp4 28.4 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/007 Testing the Network.mp4 28.3 MB
  • 39 - GPT/004 Byte-Pair Encoding.mp4 28.3 MB
  • 39 - GPT/011 (4) GPT Implementation Part 1.mp4 27.3 MB
  • 05 - Optimization/008 RMSProp.mp4 27.3 MB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/004 Cosine Annealing with Warm Restarts.mp4 26.9 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/007 Testing the CNN.mp4 26.1 MB
  • 37 - BERT/001 What is BERT and its structure.mp4 25.5 MB
  • 24 - Neural Style Transfer/002 NST Theory Part 2.mp4 24.9 MB
  • 33 - Transformers/013 Cross Entropy Loss.mp4 24.4 MB
  • 36 - Google Colab and Gradient Accumulation/001 Running your models on Google Colab.mp4 23.9 MB
  • 04 - Regularization and Normalization/002 L1 and L2 Regularization.mp4 23.7 MB
  • 14 - Convolutional Neural Networks/006 More on Convolutions.mp4 23.6 MB
  • 01 - How Neural Networks and Backpropagation Works/009 Backpropagation Part 1.mp4 22.8 MB
  • 18 - Transposed Convolutions/001 Introduction to Transposed Convolutions.mp4 22.6 MB
  • 33 - Transformers/017 Learning Rate Warmup.mp4 22.3 MB
  • 11 - Visualize the Learning Process/007 Neural Networks Playground.mp4 22.0 MB
  • 11 - Visualize the Learning Process/003 Visualize Learning Part 3.mp4 21.8 MB
  • 39 - GPT/006 Playing with HuggingFace models.mp4 21.8 MB
  • 30 - Sequence Modelling/002 Image Captioning.mp4 21.4 MB
  • 16 - CNN Architectures/010 Transfer Learning.mp4 21.3 MB
  • 34 - Build a Chatbot with Transformers/022 Action.mp4 21.3 MB
  • 14 - Convolutional Neural Networks/015 Softmax with Temperature.mp4 21.2 MB
  • 02 - Loss Functions/003 Huber Loss.mp4 21.2 MB
  • 01 - How Neural Networks and Backpropagation Works/010 Backpropagation Part 2.mp4 21.1 MB
  • 26 - Recurrent Neural Networks/009 GRUs.mp4 21.0 MB
  • 14 - Convolutional Neural Networks/008 A Tool for Convolution Visualization.mp4 20.9 MB
  • 22 - Autoencoders and Variational Autoencoders/002 Denoising Autoencoders.mp4 20.8 MB
  • 05 - Optimization/006 Bias Correction in Exponentially Weighted Averages.mp4 20.7 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/005 Understanding Pack Padded Sequence.mp4 20.5 MB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/002 Code Details.mp4 20.2 MB
  • 03 - Activation Functions/006 Gated Linear Units (GLU).mp4 19.9 MB
  • 04 - Regularization and Normalization/008 Group Normalization.mp4 19.9 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/014 Results.mp4 19.7 MB
  • 02 - Loss Functions/008 KL divergence Loss.mp4 19.6 MB
  • 11 - Visualize the Learning Process/001 Visualize Learning Part 1.mp4 19.5 MB
  • 33 - Transformers/008 Residual Learning.mp4 19.4 MB
  • 21 - YOLO Object Detection (Theory)/004 YOLO Theory Part 4.mp4 19.3 MB
  • 05 - Optimization/007 Momentum.mp4 19.3 MB
  • 33 - Transformers/011 Masked MultiHead Attention.mp4 19.2 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/005 Understanding the Propagation.mp4 19.1 MB
  • 33 - Transformers/014 KL Divergence Loss.mp4 18.5 MB
  • 02 - Loss Functions/005 Cross Entropy Loss.mp4 18.1 MB
  • 21 - YOLO Object Detection (Theory)/010 YOLO Theory Part 10.mp4 18.0 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/002 Understanding the Implementation.mp4 18.0 MB
  • 14 - Convolutional Neural Networks/002 Introduction to Convolutional Networks and the need for them.mp4 17.7 MB
  • 12 - Implementing a Neural Network from Scratch with Numpy/005 Prediction.mp4 17.6 MB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/005 Batch Size vs Learning Rate.mp4 17.5 MB
  • 04 - Regularization and Normalization/001 Overfitting.mp4 17.4 MB
  • 03 - Activation Functions/001 Why we need activation functions.mp4 17.4 MB
  • 39 - GPT/008 (1) GPT Implementation Part 1.mp4 16.9 MB
  • 37 - BERT/002 Masked Language Modelling.mp4 16.4 MB
  • 11 - Visualize the Learning Process/004 Visualize Learning Part 4.mp4 15.7 MB
  • 03 - Activation Functions/004 ReLU and PReLU.mp4 15.7 MB
  • 35 - Universal Transformers/001 Universal Transformers.mp4 15.6 MB
  • 03 - Activation Functions/002 Sigmoid Activation.mp4 15.5 MB
  • 33 - Transformers/009 Layer Normalization.mp4 15.3 MB
  • 05 - Optimization/004 Exponentially Weighted Average Intuition.mp4 15.1 MB
  • 26 - Recurrent Neural Networks/010 CNN-LSTM.mp4 15.0 MB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/009 Teacher Forcing.mp4 14.6 MB
  • 02 - Loss Functions/001 Mean Squared Error (MSE).mp4 14.2 MB
  • 34 - Build a Chatbot with Transformers/005 Dataset Preprocessing Part 4.mp4 14.2 MB
  • 14 - Convolutional Neural Networks/013 Regularization and Batch Normalization in CNNs.mp4 14.0 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/004 Defining the Model.mp4 13.6 MB
  • 14 - Convolutional Neural Networks/005 Convolution over Volume Animation.mp4 13.3 MB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/009 Predicting an image.mp4 13.3 MB
  • 07 - Weight Initialization/001 Normal Distribution.mp4 13.2 MB
  • 26 - Recurrent Neural Networks/001 Why do we need RNNs.mp4 12.9 MB
  • 21 - YOLO Object Detection (Theory)/009 YOLO Theory Part 9.mp4 12.8 MB
  • 05 - Optimization/002 Stochastic Gradient Descent.mp4 12.7 MB
  • 30 - Sequence Modelling/003 Attention Mechanisms.mp4 12.6 MB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/001 Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 12.4 MB
  • 33 - Transformers/010 Feed Forward.mp4 11.7 MB
  • 26 - Recurrent Neural Networks/003 Quiz Solution Discussion.mp4 11.5 MB
  • 03 - Activation Functions/003 Tanh Activation.mp4 11.1 MB
  • 04 - Regularization and Normalization/004 DropConnect.mp4 10.6 MB
  • 14 - Convolutional Neural Networks/011 Important formulas.mp4 10.3 MB
  • 22 - Autoencoders and Variational Autoencoders/003 The Problem in Autoencoders.mp4 10.1 MB
  • 33 - Transformers/015 Label Smoothing.mp4 10.0 MB
  • 26 - Recurrent Neural Networks/008 Bidirectional RNNs.mp4 9.9 MB
  • 04 - Regularization and Normalization/005 Normalization.mp4 9.7 MB
  • 16 - CNN Architectures/006 CNN Architectures Part 2.mp4 9.5 MB
  • 03 - Activation Functions/007 Swish Activation.mp4 9.5 MB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/013 Training.mp4 9.4 MB
  • 14 - Convolutional Neural Networks/010 CNN Visualization.mp4 9.3 MB
  • 07 - Weight Initialization/004 He Norm Initialization.mp4 9.3 MB
  • 27 - Word Embeddings/002 Visualizing Word Embeddings.mp4 9.2 MB
  • 11 - Visualize the Learning Process/002 Visualize Learning Part 2.mp4 8.8 MB
  • 03 - Activation Functions/005 Exponentially Linear Units (ELU).mp4 8.6 MB
  • 33 - Transformers/007 Concat and Linear.mp4 7.7 MB
  • 27 - Word Embeddings/004 Word Embeddings Models.mp4 7.6 MB
  • 33 - Transformers/012 MultiHead Attention in Decoder.mp4 7.5 MB
  • 05 - Optimization/010 SWATS - Switching from Adam to SGD.mp4 6.7 MB
  • 26 - Recurrent Neural Networks/005 Stacked RNNs.mp4 6.0 MB
  • 05 - Optimization/003 Mini-Batch Gradient Descent.mp4 5.2 MB
  • 14 - Convolutional Neural Networks/007 Quiz Solution Discussion.mp4 4.7 MB
  • 27 - Word Embeddings/003 Measuring Word Embeddings.mp4 4.2 MB
  • 08 - Introduction to PyTorch/001 CODE FOR THIS COURSE.mp4 1.2 MB
  • 20 - Convolutional Networks Visualization/19339744-dog.jpg 95.5 kB
  • 22 - Autoencoders and Variational Autoencoders/005 Probability Distributions Recap.vtt 39.4 kB
  • 20 - Convolutional Networks Visualization/13787548-imagenet-class-index.json 35.4 kB
  • 22 - Autoencoders and Variational Autoencoders/006 Loss Function Derivation for VAE.vtt 34.5 kB
  • 08 - Introduction to PyTorch/009 Loss Functions in PyTorch.vtt 33.8 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/006 Training the Network.vtt 29.6 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/003 Building the CNN.vtt 29.2 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/004 Defining the Encoder.vtt 28.5 kB
  • 26 - Recurrent Neural Networks/007 LSTMs.vtt 26.1 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/008 Backpropagation.vtt 25.3 kB
  • 23 - Practical Variational Autoencoders in PyTorch/001 Practical VAE Part 1.vtt 23.4 kB
  • 34 - Build a Chatbot with Transformers/017 Loss with Label Smoothing.vtt 22.8 kB
  • 08 - Introduction to PyTorch/004 How PyTorch Works.vtt 21.9 kB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/006 Part 5 Training the Network.vtt 21.2 kB
  • 16 - CNN Architectures/003 Residual Networks Part 2.vtt 21.2 kB
  • 09 - Data Augmentation/003 2_Data Augmentation Techniques Part 2.vtt 21.1 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/006 Creating the Encoder.vtt 20.8 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/008 Designing the Decoder Part 2.vtt 20.8 kB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/005 Part 4 Building the Network.vtt 20.6 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/007 Creating the Decoder Part 1.vtt 20.5 kB
  • 01 - How Neural Networks and Backpropagation Works/005 The Perceptron.vtt 19.6 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/012 Evaluation Function.vtt 19.5 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/004 Loss Function.vtt 19.3 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/006 Training the CNN.vtt 19.2 kB
  • 34 - Build a Chatbot with Transformers/020 Evaluation Function.vtt 19.0 kB
  • 36 - Google Colab and Gradient Accumulation/002 Gradient Accumulation.vtt 18.9 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/006 Designing the Attention Model.vtt 18.8 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/010 Train Function.vtt 18.5 kB
  • 37 - BERT/005 Exploring Transformers.vtt 18.3 kB
  • 34 - Build a Chatbot with Transformers/003 Dataset Preprocessing Part 2.vtt 18.3 kB
  • 29 - Saving and Loading Models/001 Saving and Loading Part 1.vtt 17.8 kB
  • 39 - GPT/013 (6) GPT Implementation Part 1.vtt 17.7 kB
  • 34 - Build a Chatbot with Transformers/008 Embeddings.vtt 17.3 kB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/002 Part 1 Data Preprocessing.vtt 17.1 kB
  • 25 - Practical Neural Style Transfer in PyTorch/004 NST Practical Part 4.vtt 16.9 kB
  • 01 - How Neural Networks and Backpropagation Works/002 What Can Deep Learning Do.vtt 16.9 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/011 Defining Hyperparameters.vtt 16.8 kB
  • 33 - Transformers/004 Positional Encoding.vtt 16.7 kB
  • 16 - CNN Architectures/007 Densely Connected Networks.vtt 16.7 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/007 Designing the Decoder Part 1.vtt 16.7 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/002 Utility Functions.vtt 16.7 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/004 Constructing the Dataset Part 1.vtt 16.6 kB
  • 39 - GPT/014 (7) GPT Implementation Part 1.vtt 16.6 kB
  • 16 - CNN Architectures/005 Stochastic Depth.vtt 16.5 kB
  • 39 - GPT/009 (2) GPT Implementation Part 1.vtt 16.4 kB
  • 39 - GPT/010 (3) GPT Implementation Part 1.vtt 16.4 kB
  • 08 - Introduction to PyTorch/002 Computation Graphs and Deep Learning Frameworks.vtt 16.1 kB
  • 20 - Convolutional Networks Visualization/002 Processing the Model.vtt 16.1 kB
  • 30 - Sequence Modelling/001 Sequence Modeling.vtt 16.0 kB
  • 02 - Loss Functions/004 Binary Cross Entropy Loss.vtt 15.8 kB
  • 38 - Vision Transformers/001 Vision Transformer Part 1.vtt 15.7 kB
  • 14 - Convolutional Neural Networks/009 Activation, Pooling and FC.vtt 15.7 kB
  • 17 - Practical Residual Networks in PyTorch/004 Practical ResNet Part 4.vtt 15.6 kB
  • 35 - Universal Transformers/002 Practical Universal Transformers Modifying the Transformers code.vtt 15.6 kB
  • 34 - Build a Chatbot with Transformers/007 Data Loading and Masking.vtt 15.5 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/009 Creating the Decoder Part 3.vtt 15.5 kB
  • 26 - Recurrent Neural Networks/004 Backpropagation Through Time.vtt 15.4 kB
  • 02 - Loss Functions/010 Hinge Loss.vtt 15.3 kB
  • 02 - Loss Functions/011 Triplet Ranking Loss.vtt 15.2 kB
  • 08 - Introduction to PyTorch/010 Weight Initialization in PyTorch.vtt 15.2 kB
  • 20 - Convolutional Networks Visualization/003 Visualizing the Feature Maps.vtt 15.1 kB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/002 Step Learning Rate Decay.vtt 15.0 kB
  • 33 - Transformers/013 Cross Entropy Loss.vtt 15.0 kB
  • 28 - Practical Recurrent Networks in PyTorch/007 Generating Text.vtt 15.0 kB
  • 34 - Build a Chatbot with Transformers/011 MultiHead Attention Implementation Part 3.vtt 14.9 kB
  • 04 - Regularization and Normalization/006 Batch Normalization.vtt 14.8 kB
  • 21 - YOLO Object Detection (Theory)/002 YOLO Theory Part 2.vtt 14.8 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/007 Backpropagation Equations.vtt 14.8 kB
  • 17 - Practical Residual Networks in PyTorch/001 Practical ResNet Part 1.vtt 14.8 kB
  • 17 - Practical Residual Networks in PyTorch/002 Practical ResNet Part 2.vtt 14.8 kB
  • 02 - Loss Functions/009 Contrastive Loss.vtt 14.8 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/001 Loading and Normalizing the Dataset.vtt 14.7 kB
  • 33 - Transformers/002 Introduction to Transformers.vtt 14.7 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/001 Implementation Details.vtt 14.6 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/003 Importing and Defining Parameters.vtt 14.5 kB
  • 39 - GPT/012 (5) GPT Implementation Part 1.vtt 14.5 kB
  • 17 - Practical Residual Networks in PyTorch/003 Practical ResNet Part 3.vtt 14.4 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/001 Data Augmentation.vtt 14.3 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/001 The Dataset and Hyperparameters.vtt 14.3 kB
  • 14 - Convolutional Neural Networks/014 DropBlock Dropout in CNNs.vtt 14.3 kB
  • 38 - Vision Transformers/003 Vision Transformer Part 3.vtt 14.3 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/003 Forward Propagation.vtt 14.2 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/005 Constructing the Dataset Part 2.vtt 14.1 kB
  • 16 - CNN Architectures/001 CNN Architectures Part 1.vtt 14.1 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/010 Classifying your own Handwritten images.vtt 14.1 kB
  • 01 - How Neural Networks and Backpropagation Works/006 Gradient Descent.vtt 14.1 kB
  • 23 - Practical Variational Autoencoders in PyTorch/003 Practical VAE Part 3.vtt 14.1 kB
  • 05 - Optimization/008 RMSProp.vtt 14.0 kB
  • 16 - CNN Architectures/009 Seperable Convolutions.vtt 13.7 kB
  • 30 - Sequence Modelling/004 How Attention Mechanisms Work.vtt 13.7 kB
  • 34 - Build a Chatbot with Transformers/015 Transformer.vtt 13.7 kB
  • 08 - Introduction to PyTorch/005 Torch Tensors - Part 1.vtt 13.6 kB
  • 23 - Practical Variational Autoencoders in PyTorch/002 Practical VAE Part 2.vtt 13.5 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/004 Understanding the data.vtt 13.5 kB
  • 25 - Practical Neural Style Transfer in PyTorch/003 NST Practical Part 3.vtt 13.4 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/008 Creating the Decoder Part 2.vtt 13.4 kB
  • 08 - Introduction to PyTorch/007 Numpy Bridge, Tensor Concatenation and Adding Dimensions.vtt 13.4 kB
  • 01 - How Neural Networks and Backpropagation Works/009 Backpropagation Part 1.vtt 13.1 kB
  • 28 - Practical Recurrent Networks in PyTorch/005 Creating the Network.vtt 13.1 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/002 Loading the Dataset.vtt 13.1 kB
  • 08 - Introduction to PyTorch/003 Installing PyTorch and an Introduction.vtt 13.0 kB
  • 11 - Visualize the Learning Process/005 Visualize Learning Part 5.vtt 13.0 kB
  • 16 - CNN Architectures/002 Residual Networks Part 1.vtt 13.0 kB
  • 22 - Autoencoders and Variational Autoencoders/004 Variational Autoencoders.vtt 12.9 kB
  • 34 - Build a Chatbot with Transformers/019 Training Function.vtt 12.9 kB
  • 34 - Build a Chatbot with Transformers/004 Dataset Preprocessing Part 3.vtt 12.9 kB
  • 01 - How Neural Networks and Backpropagation Works/007 The Forward Propagation.vtt 12.8 kB
  • 25 - Practical Neural Style Transfer in PyTorch/001 NST Practical Part 1.vtt 12.8 kB
  • 39 - GPT/001 GPT Part 1.vtt 12.7 kB
  • 26 - Recurrent Neural Networks/006 Vanishing and Exploding Gradient Problem.vtt 12.6 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/003 Accuracy Calculation.vtt 12.6 kB
  • 24 - Neural Style Transfer/003 NST Theory Part 3.vtt 12.5 kB
  • 28 - Practical Recurrent Networks in PyTorch/003 Processing the Text.vtt 12.4 kB
  • 21 - YOLO Object Detection (Theory)/012 YOLO Theory Part 12.vtt 12.3 kB
  • 34 - Build a Chatbot with Transformers/002 Dataset Preprocessing Part 1.vtt 12.3 kB
  • 16 - CNN Architectures/008 Squeeze-Excite Networks.vtt 12.3 kB
  • 28 - Practical Recurrent Networks in PyTorch/006 Training the Network.vtt 12.2 kB
  • 33 - Transformers/005 MultiHead Attention Part 1.vtt 12.1 kB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/003 Cyclic Learning Rate.vtt 12.1 kB
  • 16 - CNN Architectures/011 Is a 1x1 convolutional filter equivalent to a FC layer.vtt 12.1 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/006 Testing and Visualizing the results.vtt 11.9 kB
  • 08 - Introduction to PyTorch/006 Torch Tensors - Part 2.vtt 11.9 kB
  • 07 - Weight Initialization/002 What happens when all weights are initialized to the same value.vtt 11.8 kB
  • 01 - How Neural Networks and Backpropagation Works/004 The Essence of Neural Networks.vtt 11.8 kB
  • 07 - Weight Initialization/003 Xavier Initialization.vtt 11.7 kB
  • 14 - Convolutional Neural Networks/015 Softmax with Temperature.vtt 11.7 kB
  • 34 - Build a Chatbot with Transformers/006 Dataset Preprocessing Part 5.vtt 11.6 kB
  • 34 - Build a Chatbot with Transformers/021 Main Function and User Evaluation.vtt 11.5 kB
  • 25 - Practical Neural Style Transfer in PyTorch/002 NST Practical Part 2.vtt 11.5 kB
  • 14 - Convolutional Neural Networks/003 Filters and Features.vtt 11.4 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/002 Visualizing and Loading the Dataset.vtt 11.4 kB
  • 21 - YOLO Object Detection (Theory)/003 YOLO Theory Part 3.vtt 11.3 kB
  • 39 - GPT/002 GPT Part 2.vtt 11.2 kB
  • 21 - YOLO Object Detection (Theory)/006 YOLO Theory Part 6.vtt 11.2 kB
  • 01 - How Neural Networks and Backpropagation Works/010 Backpropagation Part 2.vtt 11.2 kB
  • 27 - Word Embeddings/001 What are Word Embeddings.vtt 11.2 kB
  • 39 - GPT/008 (1) GPT Implementation Part 1.vtt 11.2 kB
  • 04 - Regularization and Normalization/003 Dropout.vtt 11.1 kB
  • 33 - Transformers/016 Dropout.vtt 11.1 kB
  • 11 - Visualize the Learning Process/001 Visualize Learning Part 1.vtt 11.0 kB
  • 04 - Regularization and Normalization/002 L1 and L2 Regularization.vtt 11.0 kB
  • 22 - Autoencoders and Variational Autoencoders/001 Autoencoders.vtt 11.0 kB
  • 09 - Data Augmentation/002 2_Data Augmentation Techniques Part 1.vtt 11.0 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/004 Defining the Network Class.vtt 10.9 kB
  • 38 - Vision Transformers/002 Vision Transformer Part 2.vtt 10.9 kB
  • 08 - Introduction to PyTorch/008 Automatic Differentiation.vtt 10.9 kB
  • 05 - Optimization/013 AMSGrad.vtt 10.8 kB
  • 16 - CNN Architectures/010 Transfer Learning.vtt 10.7 kB
  • 37 - BERT/003 Next Sentence Prediction.vtt 10.6 kB
  • 05 - Optimization/005 Exponentially Weighted Average Implementation.vtt 10.5 kB
  • 35 - Universal Transformers/003 Transformers for other tasks.vtt 10.4 kB
  • 18 - Transposed Convolutions/002 Convolution Operation as Matrix Multiplication.vtt 10.4 kB
  • 37 - BERT/001 What is BERT and its structure.vtt 10.3 kB
  • 39 - GPT/011 (4) GPT Implementation Part 1.vtt 10.3 kB
  • 02 - Loss Functions/002 L1 Loss (MAE).vtt 10.2 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/002 Understanding the Implementation.vtt 10.1 kB
  • 14 - Convolutional Neural Networks/012 CNN Characteristics.vtt 10.1 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/003 Modifying the Network.vtt 10.1 kB
  • 26 - Recurrent Neural Networks/002 Vanilla RNNs.vtt 10.0 kB
  • 02 - Loss Functions/005 Cross Entropy Loss.vtt 10.0 kB
  • 33 - Transformers/006 MultiHead Attention Part 2.vtt 9.7 kB
  • 39 - GPT/004 Byte-Pair Encoding.vtt 9.7 kB
  • 39 - GPT/003 Zero-Shot Predictions with GPT.vtt 9.6 kB
  • 34 - Build a Chatbot with Transformers/010 MultiHead Attention Implementation Part 2.vtt 9.6 kB
  • 36 - Google Colab and Gradient Accumulation/001 Running your models on Google Colab.vtt 9.5 kB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/003 Part 2 Data Normalization.vtt 9.5 kB
  • 11 - Visualize the Learning Process/003 Visualize Learning Part 3.vtt 9.5 kB
  • 21 - YOLO Object Detection (Theory)/005 YOLO Theory Part 5.vtt 9.5 kB
  • 09 - Data Augmentation/004 2_Data Augmentation Techniques Part 3.vtt 9.4 kB
  • 20 - Convolutional Networks Visualization/001 Data and the Model.vtt 9.4 kB
  • 04 - Regularization and Normalization/007 Layer Normalization.vtt 9.4 kB
  • 22 - Autoencoders and Variational Autoencoders/007 Deep Fake.vtt 9.3 kB
  • 29 - Saving and Loading Models/002 Saving and Loading Part 2.vtt 9.3 kB
  • 02 - Loss Functions/006 Softmax Function.vtt 9.2 kB
  • 34 - Build a Chatbot with Transformers/013 Encoder Layer.vtt 9.1 kB
  • 11 - Visualize the Learning Process/006 Visualize Learning Part 6.vtt 9.1 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/005 Understanding Pack Padded Sequence.vtt 8.9 kB
  • 39 - GPT/006 Playing with HuggingFace models.vtt 8.9 kB
  • 02 - Loss Functions/008 KL divergence Loss.vtt 8.9 kB
  • 28 - Practical Recurrent Networks in PyTorch/004 Defining and Visualizing the Parameters.vtt 8.8 kB
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/004 Part 3 Creating and Loading the Dataset.vtt 8.8 kB
  • 33 - Transformers/009 Layer Normalization.vtt 8.8 kB
  • 22 - Autoencoders and Variational Autoencoders/002 Denoising Autoencoders.vtt 8.6 kB
  • 14 - Convolutional Neural Networks/002 Introduction to Convolutional Networks and the need for them.vtt 8.6 kB
  • 05 - Optimization/011 Weight Decay.vtt 8.6 kB
  • 02 - Loss Functions/001 Mean Squared Error (MSE).vtt 8.6 kB
  • 05 - Optimization/009 Adam Optimization.vtt 8.6 kB
  • 35 - Universal Transformers/001 Universal Transformers.vtt 8.6 kB
  • 03 - Activation Functions/004 ReLU and PReLU.vtt 8.5 kB
  • 24 - Neural Style Transfer/001 NST Theory Part 1.vtt 8.5 kB
  • 37 - BERT/004 Fine-tuning BERT.vtt 8.4 kB
  • 39 - GPT/005 Technical Details of GPT.vtt 8.4 kB
  • 18 - Transposed Convolutions/001 Introduction to Transposed Convolutions.vtt 8.4 kB
  • 26 - Recurrent Neural Networks/009 GRUs.vtt 8.3 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/007 Testing the CNN.vtt 8.2 kB
  • 09 - Data Augmentation/001 1_Introduction to Data Augmentation.vtt 8.2 kB
  • 34 - Build a Chatbot with Transformers/009 MultiHead Attention Implementation Part 1.vtt 8.1 kB
  • 34 - Build a Chatbot with Transformers/016 AdamWarmup.vtt 8.1 kB
  • 21 - YOLO Object Detection (Theory)/007 YOLO Theory Part 7.vtt 8.1 kB
  • 33 - Transformers/011 Masked MultiHead Attention.vtt 8.1 kB
  • 14 - Convolutional Neural Networks/006 More on Convolutions.vtt 8.1 kB
  • 33 - Transformers/017 Learning Rate Warmup.vtt 8.0 kB
  • 07 - Weight Initialization/001 Normal Distribution.vtt 8.0 kB
  • 21 - YOLO Object Detection (Theory)/004 YOLO Theory Part 4.vtt 8.0 kB
  • 33 - Transformers/003 Input Embeddings.vtt 7.9 kB
  • 33 - Transformers/008 Residual Learning.vtt 7.8 kB
  • 34 - Build a Chatbot with Transformers/018 Defining the Model.vtt 7.7 kB
  • 18 - Transposed Convolutions/003 Transposed Convolutions.vtt 7.7 kB
  • 05 - Optimization/001 Batch Gradient Descent.vtt 7.7 kB
  • 03 - Activation Functions/002 Sigmoid Activation.vtt 7.7 kB
  • 02 - Loss Functions/003 Huber Loss.vtt 7.7 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/009 Initializing the Network.vtt 7.7 kB
  • 01 - How Neural Networks and Backpropagation Works/003 The Rise of Deep Learning.vtt 7.6 kB
  • 24 - Neural Style Transfer/002 NST Theory Part 2.vtt 7.4 kB
  • 05 - Optimization/006 Bias Correction in Exponentially Weighted Averages.vtt 7.4 kB
  • 04 - Regularization and Normalization/008 Group Normalization.vtt 7.3 kB
  • 33 - Transformers/014 KL Divergence Loss.vtt 7.2 kB
  • 27 - Word Embeddings/005 Word Embeddings in PyTorch.vtt 7.2 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/005 Creating the network class and the network functions.vtt 7.2 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/002 Introduction.vtt 7.2 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/003 Understanding the Encoder.vtt 7.2 kB
  • 05 - Optimization/007 Momentum.vtt 7.2 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/005 Understanding the Propagation.vtt 7.1 kB
  • 03 - Activation Functions/008 Mish Activation.vtt 7.0 kB
  • 29 - Saving and Loading Models/003 Saving and Loading Part 3.vtt 7.0 kB
  • 28 - Practical Recurrent Networks in PyTorch/002 Creating the Dictionary.vtt 7.0 kB
  • 21 - YOLO Object Detection (Theory)/011 YOLO Theory Part 11.vtt 6.9 kB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/004 Cosine Annealing with Warm Restarts.vtt 6.7 kB
  • 37 - BERT/002 Masked Language Modelling.vtt 6.6 kB
  • 30 - Sequence Modelling/003 Attention Mechanisms.vtt 6.6 kB
  • 11 - Visualize the Learning Process/004 Visualize Learning Part 4.vtt 6.5 kB
  • 21 - YOLO Object Detection (Theory)/008 YOLO Theory Part 8.vtt 6.5 kB
  • 05 - Optimization/004 Exponentially Weighted Average Intuition.vtt 6.4 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/005 Prediction.vtt 6.3 kB
  • 14 - Convolutional Neural Networks/011 Important formulas.vtt 6.3 kB
  • 34 - Build a Chatbot with Transformers/014 Decoder Layer.vtt 6.3 kB
  • 11 - Visualize the Learning Process/007 Neural Networks Playground.vtt 6.3 kB
  • 19 - Transfer Learning in PyTorch - Image Classification/005 Finetuning the Network.vtt 6.3 kB
  • 26 - Recurrent Neural Networks/001 Why do we need RNNs.vtt 6.2 kB
  • 21 - YOLO Object Detection (Theory)/001 YOLO Theory Part 1.vtt 6.2 kB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/001 Introduction to Hyperparameter Tuning and Learning Rate Recap.vtt 6.2 kB
  • 30 - Sequence Modelling/002 Image Captioning.vtt 6.2 kB
  • 05 - Optimization/002 Stochastic Gradient Descent.vtt 6.1 kB
  • 22 - Autoencoders and Variational Autoencoders/003 The Problem in Autoencoders.vtt 6.0 kB
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/009 Teacher Forcing.vtt 6.0 kB
  • 04 - Regularization and Normalization/001 Overfitting.vtt 6.0 kB
  • 26 - Recurrent Neural Networks/010 CNN-LSTM.vtt 6.0 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/009 Predicting an image.vtt 5.9 kB
  • 14 - Convolutional Neural Networks/001 Prerequisite Filters.vtt 5.9 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/008 Plotting and Putting into Action.vtt 5.9 kB
  • 04 - Regularization and Normalization/005 Normalization.vtt 5.7 kB
  • 33 - Transformers/015 Label Smoothing.vtt 5.6 kB
  • 14 - Convolutional Neural Networks/008 A Tool for Convolution Visualization.vtt 5.5 kB
  • 05 - Optimization/012 Decoupling Weight Decay.vtt 5.4 kB
  • 34 - Build a Chatbot with Transformers/005 Dataset Preprocessing Part 4.vtt 5.3 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/007 Testing the Network.vtt 5.2 kB
  • 15 - Practical Convolutional Networks in PyTorch - Image Classification/004 Defining the Model.vtt 5.2 kB
  • 12 - Implementing a Neural Network from Scratch with Numpy/010 Training the Model.vtt 4.9 kB
  • 21 - YOLO Object Detection (Theory)/009 YOLO Theory Part 9.vtt 4.8 kB
  • 26 - Recurrent Neural Networks/008 Bidirectional RNNs.vtt 4.8 kB
  • 03 - Activation Functions/007 Swish Activation.vtt 4.8 kB
  • 03 - Activation Functions/001 Why we need activation functions.vtt 4.8 kB
  • 26 - Recurrent Neural Networks/003 Quiz Solution Discussion.vtt 4.8 kB
  • 25 - Practical Neural Style Transfer in PyTorch/005 Fast Neural Style Transfer.vtt 4.8 kB
  • 07 - Weight Initialization/004 He Norm Initialization.vtt 4.6 kB
  • 03 - Activation Functions/005 Exponentially Linear Units (ELU).vtt 4.5 kB
  • 14 - Convolutional Neural Networks/013 Regularization and Batch Normalization in CNNs.vtt 4.4 kB
  • 16 - CNN Architectures/006 CNN Architectures Part 2.vtt 4.3 kB
  • 14 - Convolutional Neural Networks/007 Quiz Solution Discussion.vtt 4.2 kB
  • 14 - Convolutional Neural Networks/005 Convolution over Volume Animation.vtt 4.2 kB
  • 34 - Build a Chatbot with Transformers/012 Feed Forward Implementation.vtt 4.1 kB
  • 27 - Word Embeddings/002 Visualizing Word Embeddings.vtt 4.0 kB
  • 33 - Transformers/010 Feed Forward.vtt 4.0 kB
  • 27 - Word Embeddings/004 Word Embeddings Models.vtt 3.9 kB
  • 03 - Activation Functions/003 Tanh Activation.vtt 3.8 kB
  • 06 - Hyperparameter Tuning and Learning Rate Scheduling/005 Batch Size vs Learning Rate.vtt 3.8 kB
  • 33 - Transformers/007 Concat and Linear.vtt 3.7 kB
  • 03 - Activation Functions/006 Gated Linear Units (GLU).vtt 3.7 kB
  • 34 - Build a Chatbot with Transformers/022 Action.vtt 3.7 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/014 Results.vtt 3.3 kB
  • 26 - Recurrent Neural Networks/005 Stacked RNNs.vtt 3.3 kB
  • 33 - Transformers/012 MultiHead Attention in Decoder.vtt 3.2 kB
  • 05 - Optimization/003 Mini-Batch Gradient Descent.vtt 3.2 kB
  • 32 - Practical Sequence Modelling in PyTorch Image Captioning/013 Training.vtt 3.1 kB
  • 21 - YOLO Object Detection (Theory)/010 YOLO Theory Part 10.vtt 2.7 kB
  • 14 - Convolutional Neural Networks/010 CNN Visualization.vtt 2.5 kB
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/002 Code Details.vtt 2.5 kB
  • 27 - Word Embeddings/003 Measuring Word Embeddings.vtt 2.4 kB
  • 11 - Visualize the Learning Process/002 Visualize Learning Part 2.vtt 2.3 kB
  • 04 - Regularization and Normalization/004 DropConnect.vtt 2.1 kB
  • 05 - Optimization/010 SWATS - Switching from Adam to SGD.vtt 1.9 kB
  • 21 - YOLO Object Detection (Theory)/013 YOLO Code Note.html 1.4 kB
  • 08 - Introduction to PyTorch/001 CODE FOR THIS COURSE.vtt 636 Bytes
  • 01 - How Neural Networks and Backpropagation Works/001 BEFORE STARTING...PLEASE READ THIS.html 630 Bytes
  • 12 - Implementing a Neural Network from Scratch with Numpy/006 Notebook for the following Lecture.html 532 Bytes
  • 13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/001 The MNIST Dataset.html 470 Bytes
  • 02 - Loss Functions/007 Softmax with Temperature Controlling your distribution.html 394 Bytes
  • 01 - How Neural Networks and Backpropagation Works/008 Before Proceeding with the Backpropagation.html 341 Bytes
  • 10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/001 Download the Dataset.html 322 Bytes
  • 14 - Convolutional Neural Networks/004 Convolution over Volume Animation Resource.html 321 Bytes
  • 28 - Practical Recurrent Networks in PyTorch/001 Download the Dataset.html 312 Bytes
  • 33 - Transformers/001 SANITY CHECK ON PREVIOUS SECTIONS.html 272 Bytes
  • 34 - Build a Chatbot with Transformers/001 CODE.html 268 Bytes
  • 31 - Practical Sequence Modelling in PyTorch Chatbot Application/001 Download the Dataset.html 252 Bytes
  • 07 - Weight Initialization/005 Practical Weight Initialization Note.html 186 Bytes
  • 02 - Loss Functions/012 Practical Loss Functions Note.html 179 Bytes
  • 39 - GPT/007 Implementation.html 128 Bytes
  • 16 - CNN Architectures/004 Note on Residual Networks Implementation.html 109 Bytes
  • 19 - Transfer Learning in PyTorch - Image Classification/external-assets-links.txt 71 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！