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Hands-On Neural Networks with Keras
Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

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Profile Icon Purkait
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$19.99 per month
Paperback Mar 2019 462 pages 1st Edition
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$43.99
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Arrow left icon
Profile Icon Purkait
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$19.99 per month
Paperback Mar 2019 462 pages 1st Edition
eBook
$29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$29.99
Paperback
$43.99
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Renews at $19.99p/m

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Hands-On Neural Networks with Keras

Section 1: Fundamentals of Neural Networks

This section familiarizes the reader with the basics of operating neural networks, how to select appropriate data, normalize features, and execute a data processing pipeline from scratch. Readers will learn how to pair ideal hyperparameters with appropriate activation, loss functions, and optimizers. Once completed, readers will have experienced working with real-world data to architect and test deep learning models on the most prominent frameworks.

This section comprises the following chapters:

  • Chapter 1, Overview of Neural Networks
  • Chapter 2, Deeper Dive into Neural Networks
  • Chapter 3, Signal Processing – Data Analysis with Neural Networks
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Key benefits

  • Design and create neural network architectures on different domains using Keras
  • Integrate neural network models in your applications using this highly practical guide
  • Get ready for the future of neural networks through transfer learning and predicting multi network models

Description

Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.

Who is this book for?

This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.

What you will learn

  • Understand the fundamental nature and workflow of predictive data modeling
  • Explore how different types of visual and linguistic signals are processed by neural networks
  • Dive into the mathematical and statistical ideas behind how networks learn from data
  • Design and implement various neural networks such as CNNs, LSTMs, and GANs
  • Use different architectures to tackle cognitive tasks and embed intelligence in systems
  • Learn how to generate synthetic data and use augmentation strategies to improve your models
  • Stay on top of the latest academic and commercial developments in the field of AI

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 30, 2019
Length: 462 pages
Edition : 1st
Language : English
ISBN-13 : 9781789536089
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Product Details

Publication date : Mar 30, 2019
Length: 462 pages
Edition : 1st
Language : English
ISBN-13 : 9781789536089
Category :
Languages :
Tools :

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Table of Contents

15 Chapters
Section 1: Fundamentals of Neural Networks Chevron down icon Chevron up icon
Overview of Neural Networks Chevron down icon Chevron up icon
A Deeper Dive into Neural Networks Chevron down icon Chevron up icon
Signal Processing - Data Analysis with Neural Networks Chevron down icon Chevron up icon
Section 2: Advanced Neural Network Architectures Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Long Short-Term Memory Networks Chevron down icon Chevron up icon
Reinforcement Learning with Deep Q-Networks Chevron down icon Chevron up icon
Section 3: Hybrid Model Architecture Chevron down icon Chevron up icon
Autoencoders Chevron down icon Chevron up icon
Generative Networks Chevron down icon Chevron up icon
Section 4: Road Ahead Chevron down icon Chevron up icon
Contemplating Present and Future Developments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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