Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

by Leonardo De Marchi, Laura Mitchell

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Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras

Key Features
  • Explore neural network architecture and understand how it functions
  • Learn algorithms to solve common problems using back propagation and perceptrons
  • Understand how to apply neural networks to applications with the help of useful illustrations
Book Description

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.

What you will learn
  • 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
Who this book is for

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.

Product Details

ISBN-13: 9781788999885
Publisher: Packt Publishing
Publication date: 05/30/2019
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 280
File size: 24 MB
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About the Author

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.

Table of Contents

Table of Contents
  1. Getting started with Supervised Learning
  2. Neural Network fundamentals
  3. Convolutional Neural Networks  for image processing
  4. Exploiting text embedding
  5. Working with RNN
  6. Reusing Neural Networks with Transfer Learning
  7. Working with Generative Algorithms  
  8. Implementing Autoencoders
  9. Working with Deep Belief Networks
  10. Monte Carlo and Reinforcement Learning
  11. What’s Next?

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