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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

Getting Started with Deep Learning

Artificial intelligence might work, and if it does, it will be the biggest development in technology ever.
– Sam Altman

Welcome to the Hands-On Deep Learning Architectures with Python! If you are completely unfamiliar with deep learning, you can begin your journey right here with this book. And for readers who have an idea about it, we have covered almost every aspect of deep learning. So you are definitely going to learn a lot more about deep learning from this book.

The book is laid out in a cumulative manner; that is, it begins from the basics and builds it over and over to get to advanced levels. In this chapter, we discuss how humans started creating intelligence in machines and how artificial intelligence gradually evolved to machine learning and eventually deep learning. We then see some nice applications of deep learning. Moving...

Artificial intelligence

Ever since the beginning of the computer era, humans have been trying to mimic the brain into the machine. Researchers have been developing methods that would make machines not only compute but also decide like we humans do. This quest of ours gave birth to artificial intelligence around the 1960s. By definition, artificial intelligence means developing systems that are capable of accomplishing tasks without a human explicitly programming every decision. In 1956, the first program for playing checkers was written by Arthur Samuel. Since then, researchers tried to mimic human intelligence by defining sets of handwritten rules that didn't involve any learning. Artificial intelligence programs, which played games such as chess, were nothing but sets of manually defined moves and strategies. In 1959, Arthur Samuel coined the term machine...

Deep learning

Though machine learning has provided computers with the capability to learn decision boundaries, it misses out on the robustness of doing so. Machine learning models have to be very specifically designed for every particular application. People spent hours deciding what features to select for optimal learning. As the data cross folded and non-linearity in data increased, machine learning models struggled to produce accurate results. Scientists soon realized that a much more powerful tool was required to apex this growth. In the 1980s, the concept of ANN was reborn, and with faster computing capabilities, deeper versions of ANN were developed, providing us with the powerful tool we were looking for—deep learning!

Applications of deep learning

...

Building the fundamentals

This section is where you will begin the journey of being a deep learning architect. Deep learning stands on the pillar of ANNs. Our first step should be to understand how they work. In this section, we describe the biological inspiration behind the artificial neuron and the mathematical model to create an ANN. We have tried keeping the mathematics to a minimum and focused more on concepts. However, we assume you are familiar with basic algebra and calculus.

Biological inspiration 

As we mentioned earlier, deep learning is inspired by the human brain. This seems a good idea indeed. To develop the intelligence of the brain inside a machine, you need the machine to mimic the brain! Now, if you...

TensorFlow and Keras

Before proceeding any further, let us quickly set up our coding environment. This book uses Python programming language all throughout the chapters. So, we expect you to have prior knowledge of Python. We will be using two of the most popular deep learning open source frameworks—TensorFlow and Keras. Let's begin with setting up Python first (in case you don't have it installed already).

We highly recommend using a Linux (Ubuntu preferably) or macOS operating system. The reason for this is most of the libraries for deep learning are built to work best with a Linux/Unix operating system. All the setup instructions will be covered for these operating systems.

While installing Python, it is recommended to install version 3.6 rather than the latest 3.7 or beyond. This is to avoid unpredicted conflicts between TensorFlow and Python due to...

Summary

Let's take a quick look at what we learned in this chapter. We began by briefly discussing artificial intelligence and its evolution through machine learning and then deep learning. We then saw details about some interesting applications of deep learning like machine translation, chatbots, and optical character recognition. This being the first chapter of the book, we focus on learning the fundamentals for deep learning.

We learned how ANN works with the help of some mathematics. Also, we saw different types of activation functions used in ANN and deep learning. Finally, we moved to set our coding environment with TensorFlow and Keras for building deep learning models.

In the next chapter, we will see how neural networks evolved into deep feedforward networks and deep learning. We will also code our first deep learning model with TensorFlow and Keras!

...
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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

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Language : English
ISBN-13 : 9781788998086
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Google
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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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