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Deep Learning for Beginners

Deep Learning for Beginners

By : Pablo Rivas, Rivas
4.3 (3)
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Deep Learning for Beginners

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.
Table of Contents (20 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Getting Up to Speed
Section 1: Getting Up to Speed
Free Chapter
2
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
3
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
4
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
5
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
6
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
7
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
8
Section 2: Unsupervised Deep Learning
Section 2: Unsupervised Deep Learning
9
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
10
Deep Autoencoders
Deep Autoencoders
Introducing deep belief networks
Making deep autoencoders
Exploring latent spaces with deep autoencoders
Summary
Questions and answers
References
11
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
12
Restricted Boltzmann Machines
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Restricted Boltzmann Machines
Introduction to RBMs
Learning data representations with RBMs
Comparing RBMs and AEs
Summary
Questions and answers
References
13
Section 3: Supervised Deep Learning
Section 3: Supervised Deep Learning
14
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
15
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
16
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
17
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
18
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
19
Other Books You May Enjoy
Other Books You May Enjoy
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Questions and answers

  1. Why can't we perform data reconstructions with an RBM?

RBMs are fundamentally different to AEs. An RBM aims to optimize an energy function, while an AE aims to optimize a data reconstruction function. Thus, we can't do reconstructions with RBMs. However, this fundamental difference allows for new latent spaces that are interesting and robust.

  1. Can we add more layers to an RBM?

No. Not in the current model presented here. The concept of stacked layers of neurons fits the concept of deep AEs better.

  1. What is so cool about RBMs then?

They are simple. They are fast. They provide rich latent spaces. They have no equal at this point. The closest competitors are AEs.

End of Section 6
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