Hands-On Neural Networks

This is the code repository for Hands-On Neural Networks, published by Packt.

Learn how to build and train your first neural network model using Python

What is this book about?

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics.

Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks.

By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.

This book covers the following exciting features:

Learn how to train a network by using backpropagation
Discover how to load and transform images for use in neural networks
Study how neural networks can be applied to a varied set of applications
Solve common challenges faced in neural network development
Understand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) network
Get up to speed with advanced and complex deep learning concepts like LSTMs and NLP
Explore innovative algorithms like GANs and deep reinforcement learning

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

class Perceptron(object):
"""
Simple implementation of the perceptron algorithm
"""

def __init__(self, w0=1, w1=0.1, w2=0.1):

# weights
self.w0 = w0 # bias
self.w1 = w1
self.w2 = w2

Following is what you need for this book: If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12).

Software and Hardware List

Chapter	Software required	OS required
All	Python 3.7	Windows, Mac OS X, and Linux (Any)
All	Anaconda 5.2	Windows, Mac OS X, and Linux (Any)
All	TensorFlow	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Authors

Leonardo De Marchi is an international speaker, author and consultant. He holds a masters in artificial intelligence (AI) and has worked as a data scientist in the sporting world, with clients such as New York Knicks, Manchester United. He now works as a head of data scientist at Badoo, the largest dating site with over 400 million users. He is also the lead instructor at ideai.io, a company specialized in Machine Learning trainings. With Ideai he provides technical and managerial training to large institutions and dynamic startups. He is also a contractor for the European Commission.

Laura Mitchell graduated with a degree in mathematics from the University of Edinburgh and, since then, has gained over 12 years' experience in the tech and data science space. She is currently lead data scientist at Badoo, which is the largest online dating site in the world with over 400 million users worldwide. Laura has hands-on experience in the delivery of projects such as NLP, image classification, and recommender systems, from initial conception through to production. She has a passion for learning new technologies and keeping up to date with industry trends.

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