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The Nuts and Bolts of Neural Networks

In this chapter, we'll discuss some of the intricacies of neural networks (NNs)—the cornerstone of deep learning (DL). We'll talk about their mathematical apparatus, structure, and training. Our main goal is to provide you with a systematic understanding of NNs. Often, we approach them from a computer science perspective—as a machine learning (ML) algorithm (or even a special entity) composed of a number of different steps/components. We gain our intuition by thinking in terms of neurons, layers, and so on (at least I did this when I first learned about this field). This is a perfectly valid way to do things and we can still do impressive things at this level of understanding. Perhaps this is not the correct approach, though.

NNs have solid mathematical foundations and if we approach them from this point of view, we...

Key benefits

Get to grips with building faster and more robust deep learning architectures

Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch

Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs

Description

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

Who is this book for?

This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

What you will learn

Cover advanced and state-of-the-art neural network architectures

Understand the theory and math behind neural networks

Train DNNs and apply them to modern deep learning problems

Use CNNs for object detection and image segmentation

Implement generative adversarial networks (GANs) and variational autoencoders to generate new images

Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models

Understand DL techniques, such as meta-learning and graph neural networks

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

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SJ Nov 12, 2020

I was looking for a good book that gives insight into advanced neural networks in all domains esp NLP, GANs, and some graphical models. I think this book is a great starter book for it. After reading this, I think it's much easy to refer to the original research papers.

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Californian Customer Nov 04, 2020

This book goes to insane depths on the intricacies of CNNs, RNNs. At 468 pages long, there are tons of examples with PyTorch, and I especially enjoyed how it’s natively written in up-to-date Python to remain relevant.

Taipan Apr 20, 2020

Another very helpful book by the author.

Prajakta Dec 27, 2020

I am an applied machine learning engineer. Of course, theory is important. But the most important thing is, can you convert theory to code? Can you use existing frameworks or write your own implementations to go from research to AI products? This book will help you do that. It will introduce you to an array of topics in deep learning and provide sample code implementations for the same. Take these implementations and try them on 5 different datasets. That's how you learn to train models well.This book is not for beginners. But it is not advanced as well. So don't be intimidated by the name. You don't have to go through the whole book. If you are working on a text classification problem, just go through that section of the book. It'll really give you a headstart.

Yugandhar Jangale Oct 26, 2020

Great book for detailed study

Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

What do you get with Print?

Advanced Deep Learning with Python

The Nuts and Bolts of Neural Networks

The mathematical apparatus of NNs

Linear algebra

A short introduction to NNs

Neurons

Training NNs

Gradient descent

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Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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