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

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Getting Started with Supervised Learning

Artificial Intelligence (AI) is now a buzzword that is added to products and services to make them more appealing, and more often than not, it's a marketing strategy rather than a technical achievement. Most of the time, AI is used as an umbrella term to describe anything from simple analytics to advanced learning algorithms. It's something that sells, as most of the population does not have much knowledge about it, but intuitively everyone now understands that it is something that will change the world we live in.

Luckily, it's not just hype, and we have seen many astonishing achievements made by AI, such as Tesla's self-driving cars. Using recent research into deep neural networks, Tesla managed to create a functionality and made it available to the masses much quicker than most of the experts predicted.

In this book...

Key benefits

Explore neural network architecture and understand how it functions

Learn algorithms to solve common problems using backpropagation and perceptrons

Understand how to apply neural networks to applications with the help of useful illustrations

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 transfer learning concepts to solve tasks using Keras and Visual Geometry Group (VGG) network

Get up to speed with advanced and complex deep learning concepts such as LSTMs and natural language processing (NLP)

Explore innovative algorithms including GANs and deep reinforcement learning

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€29.99

Applied Unsupervised Learning with Python

€36.99

Hands-On Deep Learning Algorithms with Python

€29.99

Total € 96.97

Al Oct 11, 2019

Excellent and comprehensive insight! I love the author's big picture view and how it translated it in simple python code.

Amazon Verified review

Saadallah Kassir Jan 12, 2021

- The topics discussed are interesting and useful, although some chapters need additional explanation. Some of the concepts are indeed discussed at a very high level.- The notation may be a bit hard to follow as variables are sometimes undefined. Some terminology is not technically correct, e.g., confusion between MSE and RMSE, with a confusing expression.- The code snippets may be confusing, as some instructions may be irrelevant, e.g., instructions defining variables that are never used.- The book is printed in grayscale, which makes it harder to read the figures, although the authors indicated that all the figures are available in color online.- Several typos/missing words make some paragraphs hard to read.Overall, I like the idea of the book, but it may be worth waiting for the issue of a new edition, as several errors may be confusing to the reader.

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

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Hands-On Neural Networks

Getting Started with Supervised Learning

History of AI

An overview of machine learning

Supervised learning

Environment setup

Supervised learning in practice with Python

Feature engineering

Supervised learning algorithms

Summary

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