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Advanced Deep Learning with Python
Advanced Deep Learning with Python

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

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Advanced Deep Learning with Python

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...

The mathematical apparatus of NNs

In the next few sections, we'll discuss the mathematical branches related to NNs. Once we've done this, we'll connect them to NNs themselves.

Linear algebra

Linear algebra deals with linear equations such as and linear transformations (or linear functions) and their representations, such as matrices and vectors.

Linear algebra identifies the following mathematical objects:

  • Scalars: A single number.
  • Vectors: A one-dimensional array of numbers (or components). Each component of the array has an index. In literature, we will see vectors denoted either with a superscript arrow () or in bold (x). The following is an example of a vector:
Throughout this book, we'll mostly...

A short introduction to NNs

A NN is a function (let's denote it with f) that tries to approximate another target function, g. We can describe this relationship with the following equation:

Here, x is the input data and θ are the NN parameters (weights). The goal is to find such θ parameters with the best approximate, g. This generic definition applies for both regression (approximating the exact value of g) and classification (assigning the input to one of multiple possible classes) tasks. Alternatively, the NN function can be denoted as .

We'll start our discussion from the smallest building block of the NNthe neuron.

Neurons

The preceding definition is a bird's-eye view of a NN. Now, let...

Training NNs

In this section, we'll define training a NN as the process of adjusting its parameters (weights) θ in a way that minimizes the cost function J(θ). The cost function is some performance measurement over a training set that consists of multiple samples, represented as vectors. Each vector has an associated label (supervised learning). Most commonly, the cost function measures the difference between the network output and the label.

We'll start this section with a short recap of the gradient descent optimization algorithm. If you're already familiar with it, you can skip this.

Gradient descent

For the purposes of this section, we'll use a NN with a single regression output and mean...

Summary

We started this chapter with a tutorial on the mathematical apparatus that forms the foundation of NNs. Then, we recapped on NNs and their architecture. Along the way, we tried to explicitly connect the mathematical concepts with the various components of the NNs. We paid special attention to the various types of activation functions. Finally, we took a comprehensive look at the NN training process. We discussed gradient descent, cost functions, backpropagation, weights initialization, and SGD optimization techniques.

In the next chapter, we'll discuss the intricacies of convolutional networks and their applications in the computer vision domain.

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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

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Publication date : Dec 12, 2019
Length: 468 pages
Edition : 1st
Language : English
ISBN-13 : 9781789956177
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Product Details

Publication date : Dec 12, 2019
Length: 468 pages
Edition : 1st
Language : English
ISBN-13 : 9781789956177
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
Section 1: Core Concepts Chevron down icon Chevron up icon
The Nuts and Bolts of Neural Networks Chevron down icon Chevron up icon
Section 2: Computer Vision Chevron down icon Chevron up icon
Understanding Convolutional Networks Chevron down icon Chevron up icon
Advanced Convolutional Networks Chevron down icon Chevron up icon
Object Detection and Image Segmentation Chevron down icon Chevron up icon
Generative Models Chevron down icon Chevron up icon
Section 3: Natural Language and Sequence Processing Chevron down icon Chevron up icon
Language Modeling Chevron down icon Chevron up icon
Understanding Recurrent Networks Chevron down icon Chevron up icon
Sequence-to-Sequence Models and Attention Chevron down icon Chevron up icon
Section 4: A Look to the Future Chevron down icon Chevron up icon
Emerging Neural Network Designs Chevron down icon Chevron up icon
Meta Learning Chevron down icon Chevron up icon
Deep Learning for Autonomous Vehicles Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(23 Ratings)
5 star 78.3%
4 star 21.7%
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SJ Nov 12, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
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Taipan Apr 20, 2020
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Another very helpful book by the author.
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Prajakta Dec 27, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
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Yugandhar Jangale Oct 26, 2020
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Great book for detailed study
Amazon Verified review Amazon
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