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Introduction to Deep Learning

Deep learning is a subset of machine learning inspired by the neural networks in the human brain. It has been around for a decade, but the reason it is so popular right now is due to the computational advancements and availability of the huge volume of data. With a huge volume of data, deep learning algorithms outperform classic machine learning. It has already been transfiguring and extensively used in several interdisciplinary scientific fields such as computer vision, natural language processing (NLP), speech recognition, and many others.

In this chapter, we will learn about the following topics:

Fundamental concepts of deep learning
Biological and artificial neurons
Artificial neural network and its layers
Activation functions
Forward and backward propagation in ANN
Gradient checking algorithm
Building an artificial neural network from scratch...

Key benefits

Get up to speed with building your own neural networks from scratch

Gain insights into the mathematical principles behind deep learning algorithms

Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow

Description

Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.

Who is this book for?

If you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming will also find the book very helpful.

What you will learn

Implement basic-to-advanced deep learning algorithms

Master the mathematics behind deep learning algorithms

Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and Nadam

Implement recurrent networks, such as RNN, LSTM, GRU, and seq2seq models

Understand how machines interpret images using CNN and capsule networks

Implement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGAN

Explore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAE

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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Surender m Sep 06, 2019

It’s a great book that’s one should have in their collection. Simple language. Easy to understand with python codes.Great book to start learning.

Amazon Verified review

Reshma Dec 01, 2019

I completed first chapter. Book so far looks concise, simple and up to the point. I will recommend it to a beginner.

kalyan reddy k Aug 23, 2019

Great material for learning, One should go through this book to get the best out of the Deep learning techniques. Besides theory, one can go through the coding for each technique which helps to understand how things work. GREAT to have one.

Vikash Jan 01, 2020

Best book for mastering Deep Learning. It will give you in-depth knowledge on Basic to Advance Deep Learning algorithm with mathematics behind each algorithm. Each page made you hungry to go further reading. I recommend it to everyone who wanted to learn Deep Learning from Basic to advance level. Best book ever on Deep Learning.Thanks Sudharsan for writing this amazing book on Deep Learning.

Amazon Customer Sep 05, 2019

Literally it is the best book on Deep Learning I have ever read. Never have I thought, someone could explain DL in such a simple way that too by making all the complex math very simple. After reading this book, I am literally in love with math.This book is for all sets of audiences. Interested in DL applications? Or wanna learn DL with detailed math or wanna learn DL intuitively? This has covered everything from basic algorithms to advanced algorithms like stack gan, cycle gan, VAE, transformer and more that too with hands-on application.Loved it! Loved it! Loved it! very much.

Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow

What do you get with Print?

Hands-On Deep Learning Algorithms with Python

Introduction to Deep Learning

What is deep learning?

Biological and artificial neurons

ANN and its layers

Exploring activation functions

The sigmoid function

Forward propagation in ANN

How does ANN learn?

Debugging gradient descent with gradient checking

Putting it all together

Building a neural network from scratch

Summary

Questions

Further reading

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