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Deep Learning Essentials
Deep Learning Essentials

Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Profile Icon Di Profile Icon Anurag Bhardwaj Profile Icon Jianing Wei
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.1 (7 Ratings)
Paperback Jan 2018 284 pages 1st Edition
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Arrow left icon
Profile Icon Di Profile Icon Anurag Bhardwaj Profile Icon Jianing Wei
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.1 (7 Ratings)
Paperback Jan 2018 284 pages 1st Edition
eBook
$20.98 $29.99
Paperback
$38.99
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Renews at $19.99p/m
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Renews at $19.99p/m

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Deep Learning Essentials

Getting Yourself Ready for Deep Learning

Due to recent achievements of Artificial Neural Networks (ANNs) in different applications of artificial intelligence (AI), such as computer vision, natural language processing (NLP) and speech recognition, deep learning has emerged as the prominent technology fundamental to most real-world implementations. This chapter aims to be a starting point on how to set oneself up for experimenting with and applying deep learning techniques in the real world.

We will answer the key question as to what skills and concepts are needed to get started with deep learning. We will specifically answer following questions:

  • What skills are needed to understand and get started with deep learning?
  • What are the core concepts from linear algebra that are required for deep learning?
  • What hardware requirements exist for practical implementations of deep learning...

Basics of linear algebra

One of the most fundamental skills required to get oneself setup with deep learning is a foundational understanding of linear algebra. Though linear algebra itself is a vast subject, and covering it in full is outside the scope of this book, we will go through some important aspects of linear algebra in this chapter. Hopefully, this will give you a sufficient understanding of some core concepts and how they interplay with deep learning methodologies.

Data representation

In this section, we will look at core data structures and representations used most commonly across different linear algebra tasks. This is not meant to be a comprehensive list at all but only serves to highlight some of the prominent...

Deep learning with GPU

As the name suggests, deep learning involves learning a deeper representation of data, which requires large amounts of computational power. Such massive computational power is usually not possible with modern day CPUs. GPUs, on the other hand, lend themselves very nicely to this task. GPUs were originally designed for rendering graphics in real time. The design of a typical GPU allows for the disproportionately larger number of arithmetic logical unit (ALU), which allows them to crunch a large number of calculations in real time.

GPUs used for general purpose computation have a high data parallel architecture, which means they can process a large number of data points in parallel, leading to higher computational throughput. Each GPU is composed of thousands of cores. Each of such cores consists of a number of functional units which contain cache and ALU...

Deep learning software frameworks

Every good deep learning application needs to have several components to be able to function correctly. These include:

  • A model layer which allows a developer to design his or her own model with more flexibility
  • A GPU layer that makes it seamless for application developers to choose between GPU/CPU for its application
  • A parallelization layer that can allow the developer to scale his or her application to run on multiple devices or instances

As you can imagine, implementing these modules is not easy. Often a developer needs to spend more time on debugging implementation issues rather than the legitimate model issues. Thankfully, a number of software frameworks exist in the industry today which make deep learning application development practically the first class of its programming language.

These frameworks vary in architecture, design, and feature...

Setting up deep learning on AWS

In this section, we will show two different ways of setting up a deep learning system using Amazon Web Services (AWS).

Setup from scratch

In this section, we will illustrate how to set up a deep learning environment on an AWS EC2 GPU instance g2.2xlarge running Ubuntu Server 16.04 LTS. For this example, we will use a pre-baked Amazon Machine Image (AMI) which already has a number of software packages installed—making it easier to set up an end-end deep learning system. We will use a publicly available AMI Image ami-b03ffedf, which has following pre-installed packages:

  • CUDA 8.0
  • Anaconda 4.20 with Python 3.0
  • Keras / Theano
  1. The first step to setting up the system is to set up an AWS account...

Summary

In this chapter, we have summarized key concepts required to get started with the real-world implementation of deep learning systems. We described core concepts from linear algebra that are central to understanding the foundations of deep learning technology. We provide a hardware guide to deep learning by covering various aspects of GPU-based implementation and what is a right hardware choice for application developers. We outline a list of most popular deep learning software frameworks that exist today and provide a feature-level parity as well as a performance benchmark for them. Finally, we demonstrate how to set up a cloud-based deep learning application on AWS.

In the next chapter, we will introduce neural networks and outline a self-start module to understanding them in greater details.

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Key benefits

  • Your one-stop solution to get started with the essentials of deep learning and neural network modeling
  • Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more
  • Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner

Description

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Who is this book for?

Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

What you will learn

  • Get to grips with the core concepts of deep learning and neural networks
  • Set up deep learning library such as TensorFlow
  • Fine-tune your deep learning models for NLP and Computer Vision applications
  • Unify different information sources, such as images, text, and speech through deep learning
  • Optimize and fine-tune your deep learning models for better performance
  • Train a deep reinforcement learning model that plays a game better than humans
  • Learn how to make your models get the best out of your GPU or CPU

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Publication date : Jan 30, 2018
Length: 284 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880360
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Product Details

Publication date : Jan 30, 2018
Length: 284 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880360
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

11 Chapters
Why Deep Learning? Chevron down icon Chevron up icon
Getting Yourself Ready for Deep Learning Chevron down icon Chevron up icon
Getting Started with Neural Networks Chevron down icon Chevron up icon
Deep Learning in Computer Vision Chevron down icon Chevron up icon
NLP - Vector Representation Chevron down icon Chevron up icon
Advanced Natural Language Processing Chevron down icon Chevron up icon
Multimodality Chevron down icon Chevron up icon
Deep Reinforcement Learning Chevron down icon Chevron up icon
Deep Learning Hacks Chevron down icon Chevron up icon
Deep Learning Trends 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 Half star icon Empty star icon 3.1
(7 Ratings)
5 star 14.3%
4 star 42.9%
3 star 14.3%
2 star 0%
1 star 28.6%
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NehaJ Feb 25, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Awesome book for starters and also for the the ones who wants to refresh their basics. This books start with basics with systematic explanation through code examples. The great thing about the book is you don't find it boring at any point of time and your interest keep on growing as you go to every next page and working example. Liked last chapter the most which has latest of deep learning examples from field like bio informatics.An awesome read for deep learning enthusiast!!!!
Amazon Verified review Amazon
J. Pegg Aug 10, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I've found the information to be good, but the book could really benefit from more proofreading. Most of the issues are grammatical (which makes me believe the general editor should have read it more closely), although there are also areas where concepts and ideas are used before they're properly introduced (which makes me believe the content reviewers should have been a bit more critical, or perhaps have slightly less domain knowledge). Again, this is a good book that covers many complex topics and assumes a technical reader (which I appreciate). I found it very informative and useful, especially when I wasn't being annoyed by simple errors or turning to Wikipedia to define concepts.
Amazon Verified review Amazon
Anaxagoras Jul 26, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a great introduction to DL. Covers all the bases. Lots of tips, tricks and applications.One drawback is that nothing is covered in enough detail to truly implement a production system.Based on the level of detail, I would give this book three stars. Based on the content, I would give this book 5 stars. My rating averages the score for the content and coverage.There doesn’t appear to be a single book that covers everything in enough detail to implement a production system for both natural language processing and computer vision systems.The ImageNet bundle from pyimagesearch has everything you need to build a deep learning system for computer vision applications but the cost could be prohibitive for some. OTOH a box with four GTX 1080 TIs, 128Gb ram, a decent CPU, and the rest of the parts could easily cost $6,000. $600 for books and code is just another 10% relative to the cost of a DL box.This won’t be the only DL book you read. I’ve read most of the books on the market. My favorites include Chollet’s Keras book and Geron’s Tensorflow book as well as Adrian Rosebrock’s books which use Keras and MXNet. This one is a very useful addition to my library.You will need to read this book and at least two of Chollet, Geron, and Rosebrock to have a reasonable grasp of the important concepts for DL for computer vision. Arxiv-sanity preserver is a great way to keep up to date on the research literature.It would be helpful to have a working knowledge of Docker as well.I’m looking forward to finding a good book on PyTorch to complement my knowledge of DL frameworks.
Amazon Verified review Amazon
Amrita Dev Feb 19, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book is very well written which covers the basic along with sample code which is easy to follow. The books starts with basic algebra and example and then goes deep in the Deep Learning space.
Amazon Verified review Amazon
stephane Mar 04, 2024
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
interesting but unfortunately the examples are for tensorflow v1 . they don't work , tensorflow v1 is deprecated
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