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Deep Learning for Beginners

A beginner’s guide to getting up and running with deep learning from scratch using Python.


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This book is for people who value their time and want to get to the point and learn the deep learning recipes needed to do things.


This book is for people who want to run code that works and modify it to make it do what is needed. Code is available as Google Colabs!

AI Ethics

This book includes discussions about applied ethics for certain algorithms. No jargon. No philosophical terms. Just common sense reflections.

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Get the book on your favorite e-book reader and easily search, find, copy, and paste all the code you need.

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Focus on what you need


Foundations of Deep Learning

This will bring you up to speed on the basic concepts of learning from data, deep learning frameworks, and preparing the data to be usable in deep learning.

This section consists of the following chapters:

  • Chapter 1, Introduction to Machine Learning
  • Chapter 2, Setup and Introduction to Deep Learning Frameworks
  • Chapter 3, Preparing Data
  • Chapter 4, Learning from Data
  • Chapter 5, Training a Single Neuron
  • Chapter 6, Training Multiple Layers of Neurons

Unsupervised Deep Learning

Focuse on this to know the kind of learning algorithms known as unsupervised algorithms. Begin with simple autoencoders and move on to deeper and larger neural models.

This section consists of the following chapters:

  • Chapter 7, Autoencoders
  • Chapter 8, Deep Autoencoders
  • Chapter 9, Variational Autoencoders
  • Chapter 10, Restricted Boltzmann Machines


Supervised Deep Learning

Focus on this section and you will know how to implement basic and advanced deep learning models for classification, regression, and generating data based on learned latent spaces.

This section consists of the following chapters:

  • Chapter 11, Deep and Wide Neural Networks
  • Chapter 12, Convolutional Neural Networks
  • Chapter 13, Recurrent Neural Networks
  • Chapter 14, Generative Adversarial Networks
  • Chapter 15, Final Remarks on the Future of Deep Learning

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Launch your career

This book aims to reach out to those beginners in deep learning who are looking for a strong foundation in the basic concepts required to build deep learning models using well-known methodologies. If that sounds like you, then this book might be what you need. This book is for aspiring data scientists and deep learning engineers who want to get started with the absolute fundamentals of deep learning and neural networks.

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    Get the code

    The book will give you direct access to all the code in the book and the code that produced the figures.

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    All, 100%, of the author proceeds go to support the LatinX in AI organization. Sharing your experience with others will help support diversity in AI.


Dr. Pablo Rivas

Prof. Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim over Emacs and spaces over tabs.

United States

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