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

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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...

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

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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stephane Mar 04, 2024

interesting but unfortunately the examples are for tensorflow v1 . they don't work , tensorflow v1 is deprecated

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Neeraj Mar 04, 2019

It seems to be copy and pasted the contents from different sources.

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JM Dec 05, 2018

Really nice, good price

J. Pegg Aug 10, 2018

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.

Anaxagoras Jul 26, 2018

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.

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

What do you get with Print?

Deep Learning Essentials

Getting Yourself Ready for Deep Learning

Basics of linear algebra

Data representation

Deep learning with GPU

Deep learning software frameworks

Setting up deep learning on AWS

Setup from scratch

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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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Table of Contents

Recommendations for you

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