PyTorch Tutorials
0.2.0_4

Beginner Tutorials

  • Deep Learning with PyTorch: A 60 Minute Blitz
    • What is PyTorch?
      • Getting Started
        • Tensors
        • Operations
      • Numpy Bridge
        • Converting torch Tensor to numpy Array
        • Converting numpy Array to torch Tensor
      • CUDA Tensors
    • Autograd: automatic differentiation
      • Variable
      • Gradients
    • Neural Networks
      • Define the network
      • Loss Function
      • Backprop
      • Update the weights
    • Training a classifier
      • What about data?
      • Training an image classifier
        • 1. Loading and normalizing CIFAR10
        • 2. Define a Convolution Neural Network
        • 3. Define a Loss function and optimizer
        • 4. Train the network
        • 5. Test the network on the test data
      • Training on GPU
      • Where do I go next?
  • PyTorch for former Torch users
    • Tensors
      • Inplace / Out-of-place
      • Zero Indexing
      • No camel casing
      • Numpy Bridge
        • Converting torch Tensor to numpy Array
        • Converting numpy Array to torch Tensor
      • CUDA Tensors
    • Autograd
      • Variable
      • Gradients
    • nn package
      • Example 1: ConvNet
      • Forward and Backward Function Hooks
      • Example 2: Recurrent Net
    • Multi-GPU examples
      • DataParallel
      • Part of the model on CPU and part on the GPU
  • Learning PyTorch with Examples
    • Tensors
      • Warm-up: numpy
      • PyTorch: Tensors
    • Autograd
      • PyTorch: Variables and autograd
      • PyTorch: Defining new autograd functions
      • TensorFlow: Static Graphs
    • nn module
      • PyTorch: nn
      • PyTorch: optim
      • PyTorch: Custom nn Modules
      • PyTorch: Control Flow + Weight Sharing
    • Examples
      • Tensors
        • Warm-up: numpy
        • PyTorch: Tensors
      • Autograd
        • PyTorch: Variables and autograd
        • PyTorch: Defining new autograd functions
        • TensorFlow: Static Graphs
      • nn module
        • PyTorch: nn
        • PyTorch: optim
        • PyTorch: Custom nn Modules
        • PyTorch: Control Flow + Weight Sharing
  • Transfer Learning tutorial
    • Load Data
      • Visualize a few images
    • Training the model
      • Visualizing the model predictions
    • Finetuning the convnet
      • Train and evaluate
    • ConvNet as fixed feature extractor
      • Train and evaluate
  • Data Loading and Processing Tutorial
    • Dataset class
    • Transforms
      • Compose transforms
    • Iterating through the dataset
    • Afterword: torchvision
  • Deep Learning for NLP with Pytorch
    • Introduction to PyTorch
      • Introduction to Torch’s tensor library
        • Creating Tensors
        • Operations with Tensors
        • Reshaping Tensors
      • Computation Graphs and Automatic Differentiation
    • Deep Learning with PyTorch
      • Deep Learning Building Blocks: Affine maps, non-linearities and objectives
        • Affine Maps
        • Non-Linearities
        • Softmax and Probabilities
        • Objective Functions
      • Optimization and Training
      • Creating Network Components in Pytorch
        • Example: Logistic Regression Bag-of-Words classifier
    • Word Embeddings: Encoding Lexical Semantics
      • Getting Dense Word Embeddings
      • Word Embeddings in Pytorch
      • An Example: N-Gram Language Modeling
      • Exercise: Computing Word Embeddings: Continuous Bag-of-Words
    • Sequence Models and Long-Short Term Memory Networks
      • LSTM’s in Pytorch
      • Example: An LSTM for Part-of-Speech Tagging
      • Exercise: Augmenting the LSTM part-of-speech tagger with character-level features
    • Advanced: Making Dynamic Decisions and the Bi-LSTM CRF
      • Dynamic versus Static Deep Learning Toolkits
      • Bi-LSTM Conditional Random Field Discussion
      • Implementation Notes
      • Exercise: A new loss function for discriminative tagging

Intermediate Tutorials

  • Classifying Names with a Character-Level RNN
    • Preparing the Data
      • Turning Names into Tensors
    • Creating the Network
    • Training
      • Preparing for Training
      • Training the Network
      • Plotting the Results
    • Evaluating the Results
      • Running on User Input
    • Exercises
  • Generating Names with a Character-Level RNN
    • Preparing the Data
    • Creating the Network
    • Training
      • Preparing for Training
      • Training the Network
      • Plotting the Losses
    • Sampling the Network
    • Exercises
  • Translation with a Sequence to Sequence Network and Attention
    • Loading data files
    • The Seq2Seq Model
      • The Encoder
      • The Decoder
        • Simple Decoder
        • Attention Decoder
    • Training
      • Preparing Training Data
      • Training the Model
      • Plotting results
    • Evaluation
    • Training and Evaluating
      • Visualizing Attention
    • Exercises
  • Reinforcement Learning (DQN) tutorial
    • Replay Memory
    • DQN algorithm
      • Q-network
      • Input extraction
    • Training
      • Hyperparameters and utilities
      • Training loop
  • Writing Distributed Applications with PyTorch
    • Setup
    • Point-to-Point Communication
    • Collective Communication
    • Distributed Training
      • Our Own Ring-Allreduce
    • Advanced Topics
      • Communication Backends
      • Initialization Methods

Advanced Tutorials

  • Neural Transfer with PyTorch
    • Introduction
      • Neural what?
      • How does it work?
        • OK. How does it work?
    • PyTorch implementation
      • Packages
      • Cuda
      • Load images
      • Display images
      • Content loss
      • Style loss
      • Load the neural network
      • Input image
      • Gradient descent
  • Creating extensions using numpy and scipy
    • Parameter-less example
    • Parametrized example
  • Transfering a model from PyTorch to Caffe2 and Mobile using ONNX
    • Transfering SRResNet using ONNX
    • Running the model on mobile devices
  • Custom C extensions for pytorch
    • Step 1. prepare your C code
    • Step 2: Include it in your Python code
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