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Deep Learning with PyTorch Quick Start Guide
PyTorchisextremelypowerfulandyeteasytolearn.Itprovidesadvancedfeatures,suchassupportingmultiprocessor,distributed,andparallelcomputation.ThisbookisanexcellententrypointforthosewantingtoexploredeeplearningwithPyTorchtoharnessitspower.ThisbookwillintroduceyoutothePyTorchdeeplearninglibraryandteachyouhowtotraindeeplearningmodelswithoutanyhassle.WewillsetupthedeeplearningenvironmentusingPyTorch,andthentrainanddeploydifferenttypesofdeeplearningmodels,suchasCNN,RNN,andautoencoders.YouwilllearnhowtooptimizemodelsbytuninghyperparametersandhowtousePyTorchinmultiprocessoranddistributedenvironments.Wewilldiscusslongshort-termmemorynetwork(LSTMs)andbuildalanguagemodeltopredicttext.Bytheendofthisbook,youwillbefamiliarwithPyTorch'scapabilitiesandbeabletoutilizethelibrarytotrainyourneuralnetworkswithrelativeease.
目錄(108章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Deep Learning with PyTorch Quick Start Guide
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introduction to PyTorch
- What is PyTorch?
- Installing PyTorch
- Digital Ocean
- Tunneling in to IPython
- Amazon Web Services (AWS)
- Basic PyTorch operations
- Default value initialization
- Converting between tensors and NumPy arrays
- Slicing and indexing and reshaping
- In place operations
- Loading data
- PyTorch dataset loaders
- Displaying an image
- DataLoader
- Creating a custom dataset
- Transforms
- ImageFolder
- Concatenating datasets
- Summary
- Deep Learning Fundamentals
- Approaches to machine learning
- Learning tasks
- Unsupervised learning
- Clustering
- Principle component analysis
- Reinforcement learning
- Supervised learning
- Classification
- Evaluating classifiers
- Features
- Handling text and categories
- Models
- Linear algebra review
- Linear models
- Gradient descent
- Multiple features
- The normal equation
- Logistic regression
- Nonlinear models
- Artificial neural networks
- The perceptron
- Summary
- Computational Graphs and Linear Models
- autograd
- Computational graphs
- Linear models
- Linear regression in PyTorch
- Saving models
- Logistic regression
- Activation functions in PyTorch
- Multi-class classification example
- Summary
- Convolutional Networks
- Hyper-parameters and multilayered networks
- Benchmarking models
- Convolutional networks
- A single convolutional layer
- Multiple kernels
- Multiple convolutional layers
- Pooling layers
- Building a single-layer CNN
- Building a multiple-layer CNN
- Batch normalization
- Summary
- Other NN Architectures
- Introduction to recurrent networks
- Recurrent artificial neurons
- Implementing a recurrent network
- Long short-term memory networks
- Implementing an LSTM
- Building a language model with a gated recurrent unit
- Summary
- Getting the Most out of PyTorch
- Multiprocessor and distributed environments
- Using a GPU
- Distributed environments
- torch.distributed
- torch.multiprocessing
- Optimization techniques
- Optimizer algorithms
- Learning rate scheduler
- Parameter groups
- Pretrained models
- Implementing a pretrained model
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 15:00:30
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