Sign In Start Free Trial

Deep Learning for Beginners

By : Pablo Rivas, Rivas

4.3 (3)

Deep Learning for Beginners

4.3 (3)

By: Pablo Rivas, Rivas

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

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

Section 1: Getting Up to Speed

Free Chapter

Introduction to Machine Learning

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

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

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 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

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

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

Section 2: Unsupervised Deep Learning

Autoencoders

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

Deep Autoencoders

Introducing deep belief networks

Making deep autoencoders

Exploring latent spaces with deep autoencoders

Summary

Questions and answers

References

Variational 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

Summary

Questions and answers

References

Restricted Boltzmann Machines

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

Section 3: Supervised Deep Learning

Deep and Wide Neural Networks

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

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

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

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

Final Remarks on the Future of Deep Learning

Looking for advanced topics in deep learning

Learning with more resources from Packt

Summary

References

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

4.3 (3)

5 star

33.3%

4 star

66.7%

3 star

0

2 star

0

1 star

0

Questions and answers

Why is the MLP better than the perceptron model?

The larger number and layers of neurons give the MLP the advantage over the perceptron to model non-linear problems and solve much more complicated pattern recognition problems.

Why is backpropagation so important to know about?

Because it is what makes neural networks learn in the era of big data.

Does the MLP always converge?

Yes and no. It does always converge to a local minimum in terms of the loss function; however, it is not guaranteed to converge to a global minimum since, usually, most loss functions are non-convex and non-smooth.

Why should we try to optimize the hyperparameters of our models?

Because anyone can train a simple neural network; however, not everyone knows what things to change to make it better. The success of your model depends heavily on you trying different things and proving to yourself (and others) that your model is the best that it can be. This is what will make you a better...

Search

Your notes and bookmarks