Hands-On Deep Learning Architectures with Python

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Hands-On Deep Learning Architectures with Python

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Preface

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What this book covers

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Section 1: The Elements of Deep Learning

Getting Started with Deep Learning

Artificial intelligence

Machine learning

Supervised learning

Regression

Classification

Unsupervised learning

Reinforcement learning

Deep learning

Applications of deep learning

Self-driving cars

Image translation

Machine translation

Encoder-decoder structure

Chatbots

Building the fundamentals

Biological inspiration

ANNs

Activation functions

Linear activation

Sigmoid activation

Tanh activation

ReLU activation

Softmax activation

TensorFlow and Keras

Setting up the environment

Introduction to TensorFlow

Installing TensorFlow CPU

Installing TensorFlow GPU

Testing your installation

Getting to know TensorFlow

Building a graph

Creating a Session

Introduction to Keras

Sequential API

Functional API

Summary

Deep Feedforward Networks

Evolutionary path to DFNs

Architecture of DFN

Training

Loss function

Regression loss

Mean squared error (MSE)

Mean absolute error

Classification loss

Cross entropy

Gradient descent

Types of gradient descent

Batch gradient descent

Stochastic gradient descent

Mini-batch gradient descent

Backpropagation

Optimizers

Train test and validation

Training set

Validation set

Test set

Overfitting and regularization

L1 and L2 regularization

Dropout

Early stopping

Building our first DFN

MNIST fashion data

Getting the data

Visualizing data

Normalizing and splitting data

Model parameters

One-hot encoding

Building a model graph

Adding placeholders

Adding layers

Adding loss function

Adding an optimizer

Calculating accuracy

Running a session to train

The easy way

Summary

Restricted Boltzmann Machines and Autoencoders