更新时间：2021-06-11 18:20:47
封面
版权信息
About Packt
Why subscribe?
Foreword
Contributors
About the author
About the reviewers
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
Get in touch
Section 1: Getting Up to Speed
Introduction to Machine Learning
Diving into the ML ecosystem
Training ML algorithms from data
Introducing deep learning
Why is deep learning important today?
Summary
Questions and answers
References
Setup and Introduction to Deep Learning Frameworks
Introduction to Colaboratory
Introduction and setup of TensorFlow
Introduction and setup of Keras
Introduction to PyTorch
Introduction to Dopamine
Other deep learning libraries
Preparing Data
Binary data and binary classification
Categorical data and multiple classes
Real-valued data and univariate regression
Altering the distribution of data
Data augmentation
Data dimensionality reduction
Ethical implications of manipulating data
Learning from Data
Learning for a purpose
Measuring success and error
Identifying overfitting and generalization
The art behind learning
Ethical implications of training deep learning algorithms
Training a Single Neuron
The perceptron model
The perceptron learning algorithm
A perceptron over non-linearly separable data
Training Multiple Layers of Neurons
The MLP model
Minimizing the error
Finding the best hyperparameters
Section 2: Unsupervised Deep Learning
Autoencoders
Introduction to unsupervised learning
Encoding and decoding layers
Applications in dimensionality reduction and visualization
Ethical implications of unsupervised learning
Deep Autoencoders
Introducing deep belief networks
Making deep autoencoders
Exploring latent spaces with deep autoencoders
Variational Autoencoders
Introducing deep generative models
Examining the VAE model
Comparing a deep and shallow VAE on MNIST
Thinking about the ethical implications of generative models
Restricted Boltzmann Machines
Introduction to RBMs
Learning data representations with RBMs
Comparing RBMs and AEs
Section 3: Supervised Deep Learning
Deep and Wide Neural Networks
