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Hands-On Deep Learning Architectures with Python
Hands-On Deep Learning Architectures with Python

Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

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Hands-On Deep Learning Architectures with Python

Section 1: The Elements of Deep Learning

In this section, you will get an overview of deep learning with Python, and will also learn about the architectures of the deep feedforward network, the Boltzmann machine, and autoencoders. We will also practice examples based on DFN and applications of the Boltzmann machine and autoencoders, with the concrete examples based on the DL frameworks/libraries with Python, along with their benchmarks.

This section consists of the following chapters:

  • Chapter 1Getting Started with Deep Learning
  • Chapter 2Deep Feedforward Networks
  • Chapter 3Restricted Boltzmann Machines and Autoencoders
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Key benefits

  • Explore advanced deep learning architectures using various datasets and frameworks
  • Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more
  • Discover design patterns and different challenges for various deep learning architectures

Description

Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.

Who is this book for?

If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

What you will learn

  • Implement CNNs, RNNs, and other commonly used architectures with Python
  • Explore architectures such as VGGNet, AlexNet, and GoogLeNet
  • Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more
  • Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples
  • Master artificial intelligence and neural network concepts and apply them to your architecture
  • Understand deep learning architectures for mobile and embedded systems
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 30, 2019
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781788998086
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Product Details

Publication date : Apr 30, 2019
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781788998086
Vendor :
Google
Category :
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Table of Contents

14 Chapters
Section 1: The Elements of Deep Learning Chevron down icon Chevron up icon
Getting Started with Deep Learning Chevron down icon Chevron up icon
Deep Feedforward Networks Chevron down icon Chevron up icon
Restricted Boltzmann Machines and Autoencoders Chevron down icon Chevron up icon
Section 2: Convolutional Neural Networks Chevron down icon Chevron up icon
CNN Architecture Chevron down icon Chevron up icon
Mobile Neural Networks and CNNs Chevron down icon Chevron up icon
Section 3: Sequence Modeling Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Section 4: Generative Adversarial Networks (GANs) Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence Chevron down icon Chevron up icon
New Trends of Deep Learning 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 Full star icon Half star icon 4.5
(2 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
andre luis Apr 25, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Este livro faz o balanceamento da teoria e da prática, fazendo a conexão entre ambos na medida certa, nem exagerando na teoria como outras publicações o fazem, nem carecendo de explicação mínima para os comandos utilizados, que são o foco desta obra ( Tensorflow e Keras ).Obviamente é necessário um conhecimento prévio mínimo por parte do leitor sobre o assunto 'redes neurais', e o livro não desperdiça muitas páginas com essa introdução; apresentando assim no máximo uma revisão da teoria.
Amazon Verified review Amazon
Anonymus Jul 31, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
All programs are implemented using tensorflow and keras.Some of the algorithms are not explained from the scratchwhich is very important to learn deeplearning indepth
Amazon Verified review Amazon
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