更新时间：2021-07-02 15:43:43
coverpage
Title Page
Copyright
Python Deep Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Programming Environments GPU Computing Cloud Solutions and Deep Learning Frameworks
Introduction
Setting up a deep learning environment
How to do it...
Launching an instance on Amazon Web Services (AWS)
Getting ready
Launching an instance on Google Cloud Platform (GCP)
Installing CUDA and cuDNN
Installing Anaconda and libraries
Connecting with Jupyter Notebooks on a server
Building state-of-the-art production-ready models with TensorFlow
Intuitively building networks with Keras
Using PyTorch’s dynamic computation graphs for RNNs
Implementing high-performance models with CNTK
Building efficient models with MXNet
Defining networks using simple and efficient code with Gluon
Feed-Forward Neural Networks
Understanding the perceptron
Implementing a single-layer neural network
Building a multi-layer neural network
Getting started with activation functions
Experiment with hidden layers and hidden units
There's more...
Implementing an autoencoder
Tuning the loss function
Experimenting with different optimizers
Improving generalization with regularization
Adding dropout to prevent overfitting
Convolutional Neural Networks
Getting started with filters and parameter sharing
Applying pooling layers
Optimizing with batch normalization
Understanding padding and strides
Experimenting with different types of initialization
Implementing a convolutional autoencoder
Applying a 1D CNN to text
Recurrent Neural Networks
Implementing a simple RNN
Adding Long Short-Term Memory (LSTM)
Using gated recurrent units (GRUs)
Implementing bidirectional RNNs
Character-level text generation
