John Slavio - Deep Learning and Artificial Intelligence: A Beginners Guide to Neural Networks and Deep Learning

DEEP LEARNING AND ARTIFICIAL INTELLIGENCE

Author: John Slavio

DISCLAIMER

Copyright Kumar 2017

All Rights Reserved

N o part of this eBook can be transmitted or reproduced in any form including print, electronic, photocopying, scanning, mechanical, or recording without prior written permission from the author.

While the author has taken the utmost effort to ensure the accuracy of the written content, all readers are advised to follow information mentioned herein at their own risk. The author cannot be held responsible for any personal or commercial damage caused by information. All readers are encouraged to seek professional advice when needed.

ABOUT THE AUTHOR

J ohn Slavio is a programmer who is passionate about the reach of the internet and the interaction of the internet with daily devices. He has automated several home devices to make them 'smart' and connect them to high speed internet. His passions involve computer security, iOT, hardware programming and blogging.

INTRODUCTION

W elcome to this book on Deep Learning and Neural Networks. We're going to be diving into what neural networks are, what the current neural networks out there do, with an API. Once we go over how everything works and how each of these new technologies work, we will also go over the many different applications that can be applied to general life and business due to the creation of neural networks. Now I want you to realize that neural networks are not a complicated topic but it may feel like a complicated topic.

There have been a lot of news stories about how there are going to be self-driving cars, machines that make their own products, and many other different applications of neural networks that make it sound like a vastly complicated machine. However, the tool of the neural network is a very simple tool. When you hear about the applications that are being created that utilize neural networks, you are actually hearing about the amount of work that went behind making a neural network do something that's complicated but not a complicated neural network. Neural networks are extremely easy to understand as you will find throughout this book but the problem is that people have made them look complicated. Therefore, let's go ahead and demystify this subject so that you can get into the field of neural networks yourself and have some fun.

WHAT ARE NEURAL NETWORKS?

A neural network is a network of neurons that are either simulated or un-simulated. The neural networks that you hear about in the news and in the technology magazines are neural networks that are built out of machines and code but the original idea comes from the biological neural networks that we have within our own systems. The neural network is a very efficient and yet very simplistic form of logical progression that allows for extreme calculations to solve complex problems.

Types of Neural Networks

There are many different forms of a neural network due to the many different ways that a neural network could be used. For instance, the same type of neural network that would be used to recognize things inside of an image is very different from the neural network that is best used for translating language or spoken words that people use on a daily basis. You could think of the image needing a neural network that can recognize the different color spectrums inside of the photograph while, on the other hand, the language detection neural network would need to constantly change what it thinks that the context of the words you are using is and this is because language changes over time and is somewhat fluid, which means that you need to be able to change the definition of the word as people move forward. A voice recognition program, on the other hand, needs to be able to constantly change due to the constantly changing nature of language and the spoken word itself. A great example of this is Google Voice, which is a software that Google developed many years ago and that was very horrible in the beginning. However, given some time and some heavy amounts of practice, Google has managed to transform such a horrible software into one of the best speech recognition software on the planet that has the highest amount of accuracy when it comes to recognizing the speech patterns that we have. It also is able to predict the next word in the sentence using the words in the current sentence on context.

Applications

Neural networks are not created for pure convenience but due to the potential usefulness that neural networks could provide. Since we can't put ourselves into machines, being able to put our own abilities inside of machines would be extremely useful under certain circumstances. Image recognition would be able to tell whether we have a criminal inside of an airport, speech recognition would be able to detect whether someone was currently in a very stressful situation, there are neural networks that provide services to lawyers who can't simply look up the entire database that lawyers have access to in order to find that one, very specific, case that someone worked on many years ago that proves or disproves a constitutional action. The list goes on and on for the different types of applications that neural networks can provide when it comes to utilizing it in our everyday lives. It doesn't stop at being useful for civilians either because it is also useful for businesses when it comes to predicting future markets, when it comes to trying to make new logos or graphical designs, and even auto building websites from scratch that have been thoroughly tested for security problems. These neural networks could easily replace almost every job on the planet, which is another thing that a lot of the greater minds on this planet have begun to see and even fear. After all, if the robots can do all the jobs on the planet then why in the world are we needed? Don't worry, we're still far from the day where neural networks have the ability of self-consciousness, but scientists say that the day will come and once the neural network is self-conscious, it will be able to build new neural networks at far greater speeds than we currently have.