Deep Learning for Beginners

By : Pablo Rivas, Rivas

4.3 (3)

Buy this Book

Deep Learning for Beginners

4.3 (3)

By: Pablo Rivas, Rivas

Buy this Book

Overview of this book

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.

Preface

Who this book is for

What this book covers

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Section 1: Getting Up to Speed

Free Chapter

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

Summary

Questions and answers

References

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

Summary

Questions and answers

References

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

Summary

Questions and answers

References

Training a Single Neuron

The perceptron model

The perceptron learning algorithm

A perceptron over non-linearly separable data

Summary

Questions and answers

References

Training Multiple Layers of Neurons

The MLP model

Minimizing the error

Finding the best hyperparameters

Summary

Questions and answers

References

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

Summary

Questions and answers

References

Deep Autoencoders

Introducing deep belief networks

Making deep autoencoders

Exploring latent spaces with deep autoencoders

Summary

Questions and answers

References

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

Summary

Questions and answers

References

Restricted Boltzmann Machines

Introduction to RBMs

Learning data representations with RBMs

Comparing RBMs and AEs

Summary

Questions and answers

References

Section 3: Supervised Deep Learning

Deep and Wide Neural Networks

Wide neural networks

Dense deep neural networks

Sparse deep neural networks

Hyperparameter optimization

Summary

Questions and answers

References

Convolutional Neural Networks

Introduction to convolutional neural networks

Convolution in n-dimensions

Convolutional layers

Pooling strategies

Convolutional neural network for CIFAR-10

Summary

Questions and answers

References

Recurrent Neural Networks

Introduction to recurrent neural networks

Long short-term memory models

Sequence-to-vector models

Vector-to-sequence models

Sequence-to-sequence models

Ethical implications

Summary

Questions and answers

References

Generative Adversarial Networks

Introducing adversarial learning

Training a GAN

Comparing GANs and VAEs

Thinking about the ethical implications of GANs

Summary

Questions and answers

References

Final Remarks on the Future of Deep Learning

Looking for advanced topics in deep learning

Learning with more resources from Packt

Summary

References

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Setup and Introduction to Deep Learning Frameworks

At this point, you are now familiar with machine learning (ML) and deep learning (DL) - this is great! You should feel ready to begin making the preparations for writing and running your own programs. This chapter helps you in the process of setting up TensorFlow and Keras, and introduces their usefulness and purpose in deep learning. Dopamine is presented as the new reinforcement learning framework that we will use later on. This chapter also briefly introduces other deep learning libraries that are important to know.

The topics that will be covered in this chapter are as follows: