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Hands-On Neural Networks
Hands-On Neural Networks

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

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Profile Icon Leonardo De Marchi Profile Icon Laura Mitchell
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€29.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback May 2019 280 pages 1st Edition
eBook
€8.99 €23.99
Paperback
€29.99
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Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Leonardo De Marchi Profile Icon Laura Mitchell
Arrow right icon
€29.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback May 2019 280 pages 1st Edition
eBook
€8.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m

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Hands-On Neural Networks

Section 1: Getting Started

This section introduces the main deep learning concepts and instructions to set up a development environment.

The following chapters are included in this section:

  • Chapter 1, Getting Started with Supervised Learning
  • Chapter 2, Neural Network Fundamentals
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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.

Who is this book 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.

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
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 30, 2019
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781788992596
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Product Details

Publication date : May 30, 2019
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781788992596
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

15 Chapters
Section 1: Getting Started Chevron down icon Chevron up icon
Getting Started with Supervised Learning Chevron down icon Chevron up icon
Neural Network Fundamentals Chevron down icon Chevron up icon
Section 2: Deep Learning Applications Chevron down icon Chevron up icon
Convolutional Neural Networks for Image Processing Chevron down icon Chevron up icon
Exploiting Text Embedding Chevron down icon Chevron up icon
Working with RNNs Chevron down icon Chevron up icon
Reusing Neural Networks with Transfer Learning Chevron down icon Chevron up icon
Section 3: Advanced Applications Chevron down icon Chevron up icon
Working with Generative Algorithms Chevron down icon Chevron up icon
Implementing Autoencoders Chevron down icon Chevron up icon
Deep Belief Networks Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Whats Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 50%
1 star 0%
Al Oct 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent and comprehensive insight! I love the author's big picture view and how it translated it in simple python code.
Amazon Verified review Amazon
Saadallah Kassir Jan 12, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
- 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.
Amazon Verified review Amazon
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