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Introduction to Machine Learning

You have probably heard the term Machine Learning (ML) or Artificial Intelligence (AI) frequently in recent years, especially Deep Learning (DL). It may be the reason you decided to invest in this book and get to know more. Given some new, exciting developments in the area of neural networks, DL has come to be a hot area in ML. Today, it is difficult to imagine a world without quick text translation between languages, or without fast song identification. These, and many other things, are just the tip of the iceberg when it comes to the potential of DL to change your world. When you finish this book, we hope you will join the bus and ride along with amazing new applications and projects based on DL.

This chapter briefly introduces the field of ML and how it is used to solve common problems. Throughout this chapter, you will be driven to understand...

Key benefits

Understand the fundamental machine learning concepts useful in deep learning

Learn the underlying mathematical concepts as you implement deep learning models from scratch

Explore easy-to-understand examples and use cases that will help you build a solid foundation in DL

Description

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.

Who is this book for?

This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.

What you will learn

Implement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasks

Explore the role of convolutional neural networks (CNNs) in computer vision and signal processing

Discover the ethical implications of deep learning modeling

Understand the mathematical terminology associated with deep learning

Code a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent space

Implement visualization techniques to compare AEs and VAEs

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Damian Valles Nov 27, 2020

This is a great introduction book by Pablo Rivas to Deep Learning concepts. The book provides great fundamental concepts that are flexible to Sciences and Engineering domains. Also, all proceedings are donated to Latinx in AI! #deeplearning #ai

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Rahul Gupta Feb 19, 2021

“Deep Learning for Beginners” by Dr. Pablo Rivas offers a great beginner's guide to getting up and running with deep learning from scratch using Python. It provides step-by-step guided instructions with code and examples explaining basic concepts with practical examples the theory and concepts of Deep Learning in diverse fields such as computer vision, natural language processing, learning representations etc. The book is organized to provide gradual transition between supervised and unsupervised models. Google Colabs, the free website, has been used to provide deep learning tools and libraries accessible to anyone enabling them to run the code on the cloud.Chapter 1, Introduction to Machine Learning, gives an overview of machine learning and introduces the motivation behind machine learning and the commonly used terminologies. It also introduces deep learning and how it fits in the realm of artificial intelligence.Chapter 2, Setup and Introduction to Deep Learning Frameworks, explains the process of setting up TensorFlow and Keras and their usefulness in deep learning.Chapter 3, Preparing Data, introduces the main concepts behind data processing for Deep Learning. It covers essential concepts of data formatting that are categorical or real-valued, as well as techniques for augmenting data or reducing the data dimensions.Chapter 4, Learning from Data, introduces elementary concepts of the theory of deep learning, including measuring performance on regression and classification as well as the identification of underfitting and overfitting and optimizing hyperparameters.Chapter 5, Training a Single Neuron, introduces the concept of a single neuron and the perceptron model, the key to understanding basic neural models that learn from data and explains the problem of non-linearly separable data.Chapter 6, Training Multiple Layers of Neurons, covers deep learning using the multi-layer perceptron (MLP) algorithm, such as gradient descent techniques for error minimization, and hyperparameter optimization.Chapter 7, Autoencoders, describes the AE model by explaining the necessity of both encoding and decoding layers, the loss functions associated with the autoencoder problem and applies it to the dimensionality reduction problem and data visualization.Chapter 8, Deep Autoencoders, introduces the idea of deep belief networks and the significance of deep unsupervised learning. It explains the concepts by introducing deep AEs and contrasting them with shallow AEs.Chapter 9, Variational Autoencoders, covers the principles behind generative models in the unsupervised deep learning field and their importance in the production of robust models free from noise and demonstrates as to why the VAE is a better alternative to a deep AE when working with perturbed data.Chapter 10, Restricted Boltzmann Machines, covers deep belief models by presenting RBMs. The backward-forward nature of RBMs is introduced and contrasted with the forward-only nature of AEs. The chapter compares RBMs and AEs on the problem of data dimensionality reduction using visual representations of the reduced data.Chapter 11, Deep and Wide Neural Networks, explains the difference in performance and complexities of deep versus wide neural networks and introduces the concept of dense networks and sparse networks in terms of the connections between neurons.Chapter 12, Convolutional Neural Networks, introduces CNNs, starting with the convolution operation and moving towards ensemble layers of convolutional operations aiming to learn and apply data filters how to visualize the learned filters.Chapter 13, Recurrent Neural Networks, presents the concepts of recurrent networks, exposing their shortcomings to justify the existence and success of long short-term memory (LSTM) models. Sequential models are explored with applications for image processing and natural language processing.

AT Mar 24, 2021

Disclaimer: The publisher asked me to review this book & gave me a review a copy. I promise to give my honest opinion about this book.This book covers all fundamentals of ML ecosystem with hands on example codes. It guides you to set up different ML frameworks. You will understand the concepts of data cleansing & preprocessing, basic ML algorithms, tuning hyper parameters, model evaluation. It covers great details on neural network architectures like CNN, RNN, AE, VAE, and GANs. After reading this, you will have pretty good idea about ML concepts and should be able to train some ML model by yourself.

Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python

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