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DEEP LEARNING WITH PYTHON: Simple and Effective Tips and Tricks to Learn Deep Learning with Python
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2020
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Deep Learning With Python

Simple and Effective Tips and Tricks to Learn Deep Learning with Python

The contents of this book may not be reproduced, duplicated, or transmitted without direct written permission from the author.

Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.

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You cannot amend, distribute, sell, use, quote, or paraphrase any part of the content within this book without the consent of the author.

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Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.

By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of the information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.

Table of Contents

Introduction

Deep learning is fast becoming one of the most used "buzz phrases" in the world, along with 'machine learning' and 'artificial intelligence.' In fact, all three are part of one of the biggest global phenomena, data science. Deep learning is a subset of machine learning and AI, and it is a subset that is growing rapidly, increasing year on year. In the next ten years or so, it is expected that deep learning will grow 71 times over in the USA, but globally, much more than that. Who doesn't want to be a part of that? Now, more than ever, it is a great time to get involved in deep learning to assure yourselves a position in the future.

I wrote this book to give you a jump-start in the fundamentals of deep learning. To tell you how to use deep learning to your advantage and teach you the methods, the algorithms, the learning mechanisms, everything you need to know to get started in the field.

Here's what we cover in this book:

Chapter One - an introduction to deep learning, how it works and what its limitations are
Chapter Two a discussion on why we use Python for deep learning and a look at some of the more popular Python libraries used for deep learning
Chapter Three an introduction to neural networks, what they are, and how they work. We look at examples such as regression, clustering, and classification, and we look at the elements a neural network is made from.
Chapter Four next, we turn to recurrent neural networks (RNNs) and LSTM (Long Short-Term Memory) units. This takes us into feedforward networks, RNNs, and Backpropagation Through Time. Then we look at vanishing/exploding gradients, hyperparameter tuning, and remote dependencies.
Chapter Five we look at deep convolutional neural networks and how to use them for image processing tasks. We look at how CNNs work and how to do downsampling and max-pooling with them.
Chapter Six in this chapter, you get an introduction to deep reinforcement learning.
Chapter Seven now, you get to put everything into practice by building a deep learning model for employee retention predictions, using two of the more popular libraries, Keras and TensorFlow
Chapter Eight here, we use TensorFlow to build a neural network for recognizing handwritten digits
Chapter Nine Finally, we look at ways to make sure your deep learning models remain free of bugs.

What I haven't done with this book is to use one example all the way through. Instead, I have divided the book into separate chapters, each with their own information. That way, you can pick and choose the subject you want to look at, without worrying that you've missed some vital information. Basically, read this book in any order you want to. However, I must stress that I will NOT be giving you a primer on the Python computer language and I will NOT be giving you an overview of machine learning these are things you should already have experience of before you even think about deep learning.

Why You Should Learn Deep Learning

There are plenty of reasons why you should take that step and get on the deep learning bandwagon:

Better Earnings Data scientists already earn a decent salary, but, when you specialize in machine learning and deep learning, you can earn an even better salary. On average, machine and deep learning specialists earn around 15% more than a standard data scientist salary so, if you have skills in this area, your resume will certainly stand out head and shoulders above the rest, even if you are not quite at the level of specialist.
A Growing Demand it is one of the fastest-growing fields in the world, growing at a pace we can't keep up with up; the faster it grows, the more demand there is for it, it's as simple as that.
It's Time-Saving if you have any experience of machine learning, then you already know that its time-consuming to convert parameters for inputs into features that are easily read by your algorithm. And it's not the easiest job, either. With deep learning, you can use neural nets, the best way of ensuring the conversions are done automatically. Rather than having to take the time to pull histograms, color data, and lots of other metrics from an image dataset, simply give the raw images to the neural net, and it will do it all for you. Yes, that is a rather simplistic way of doing things; it is a bit more complicated than that! The primary challenge lies in getting the neural network to the stage where it can do it all for you, but this does mean you get more time to work on the algorithms, rather than having to waste time on features.
A Flexible Specialty while it's great to specialize in something specific, you do run the risk of becoming stuck in a rut; at the end of the day, you may find yourself in a field that is limited in where you can work; with deep learning, it's different. It's in demand across most industries and is used to solve all kinds of tasks, such as translation, image recognition, robotics, and so on.
It's Fun putting careers to one side, why wouldn't you want to learn something that is just so cool? Let's face it; you can use deep learning to make a machine do just about anything you want it to, without having to do much in the way of teaching.

So, are you ready to dive into deep learning?

Then let's go.

Chapter One: Introducing Deep Learning

So, what is deep learning? Well, it is a kind of artificial intelligence and machine learning that mimics human methods of gaining knowledge. It is one of the most important parts of data science and includes predictive modeling and statistics. For the data scientist whose job involved collecting large amounts of data, analyzing it and interpreting it, deep learning is incredibly beneficial, as it significantly decreases the amount of time taken to perform the process.

In simple terms, we can consider deep learning as a process that automates predictive analytics. You probably already used some machine learning algorithms, and you will know that most of these are linear. With deep learning, the algorithms are stacked up, increasing in abstraction and complexity as they go.

One of the easiest ways to understand deep learning is to think of a child speaking its first word. Well say, for arguments sake, that word is cat. The child learns what cats are and what they are not, simply by pointing at things and saying the word, cat. The parents will say, no, that isnt a cat, or yes, that is a cat and, along the way, the child will learn to identify a cat by becoming aware of the features that every cat has. Without realizing it, what this child is doing is learning a complex abstraction (the concept of a cat). He does this by building up a hierarchy, each level made from the knowledge that came from the preceding layer.

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