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Deep Learning with Keras
Deep Learning with Keras

Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python

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Profile Icon Antonio Gulli Profile Icon Sujit Pal
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (20 Ratings)
Paperback Apr 2017 318 pages 1st Edition
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Arrow left icon
Profile Icon Antonio Gulli Profile Icon Sujit Pal
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Free Trial
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (20 Ratings)
Paperback Apr 2017 318 pages 1st Edition
eBook
₱2020.99 ₱2245.99
Paperback
₱2806.99
Subscription
Free Trial
eBook
₱2020.99 ₱2245.99
Paperback
₱2806.99
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Deep Learning with Keras

Neural Networks Foundations

Artificial neural networks (briefly, nets) represent a class of machine learning models, loosely inspired by studies about the central nervous systems of mammals. Each net is made up of several interconnected neurons, organized in layers, which exchange messages (they fire, in jargon) when certain conditions happen. Initial studies were started in the late 1950s with the introduction of the perceptron (for more information, refer to the article: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, by F. Rosenblatt, Psychological Review, vol. 65, pp. 386 - 408, 1958), a two-layer network used for simple operations, and further expanded in the late 1960s with the introduction of the backpropagation algorithm, used for efficient multilayer networks training (according to the articles: Backpropagation through Time: What It Does and How to Do It, by P. J. Werbos, Proceedings of the IEEE, vol. 78, pp. 1550 - 1560, 1990, and A Fast Learning Algorithm for Deep Belief Nets, by G. E. Hinton, S. Osindero, and Y. W. Teh, Neural Computing, vol. 18, pp. 1527 - 1554, 2006). Some studies argue that these techniques have roots dating further back than normally cited (for more information, refer to the article: Deep Learning in Neural Networks: An Overview, by J. Schmidhuber, vol. 61, pp. 85 - 117, 2015). Neural networks were a topic of intensive academic studies until the 1980s, when other simpler approaches became more relevant. However, there has been a resurrection of interest starting from the mid-2000s, thanks to both a breakthrough fast-learning algorithm proposed by G. Hinton (for more information, refer to the articles: The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, Neural Networks, by S. Leven, vol. 9, 1996 and Learning Representations by Backpropagating Errors, by D. E. Rumelhart, G. E. Hinton, and R. J. Williams, vol. 323, 1986) and the introduction of GPUs, roughly in 2011, for massive numeric computation.

These improvements opened the route for modern deep learning, a class of neural networks characterized by a significant number of layers of neurons, which are able to learn rather sophisticated models based on progressive levels of abstraction. People called it deep with 3-5 layers a few years ago, and now it has gone up to 100-200.

This learning via progressive abstraction resembles vision models that have evolved over millions of years in the human brain. The human visual system is indeed organized into different layers. Our eyes are connected to an area of the brain called the visual cortex V1, which is located in the lower posterior part of our brain. This area is common to many mammals and has the role of discriminating basic properties and small changes in visual orientation, spatial frequencies, and colors. It has been estimated that V1 consists of about 140 million neurons, with 10 billion connections between them. V1 is then connected with other areas V2, V3, V4, V5, and V6, doing progressively more complex image processing and recognition of more sophisticated concepts, such as shapes, faces, animals, and many more. This organization in layers is the result of a huge number of attempts tuned over several 100 million years. It has been estimated that there are ~16 billion human cortical neurons, and about 10%-25% of the human cortex is devoted to vision (for more information, refer to the article: The Human Brain in Numbers: A Linearly Scaled-up Primate Brain, by S. Herculano-Houzel, vol. 3, 2009). Deep learning has taken some inspiration from this layer-based organization of the human visual system: early artificial neuron layers learn basic properties of images, while deeper layers learn more sophisticated concepts.

This book covers several major aspects of neural networks by providing working nets coded in Keras, a minimalist and efficient Python library for deep learning computations running on the top of either Google's TensorFlow (for more information, refer to https://www.tensorflow.org/) or University of Montreal's Theano (for more information, refer to http://deeplearning.net/software/theano/) backend. So, let's start.

In this chapter, we will cover the following topics:

  • Perceptron
  • Multilayer perceptron
  • Activation functions
  • Gradient descent
  • Stochastic gradient descent
  • Backpropagation
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Key benefits

  • Implement various deep learning algorithms in Keras and see how deep learning can be used in games
  • See how various deep learning models and practical use-cases can be implemented using Keras
  • A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

Description

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.

Who is this book for?

If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book.

What you will learn

  • Optimize step-by-step functions on a large neural network using the Backpropagation algorithm
  • Fine-tune a neural network to improve the quality of results
  • Use deep learning for image and audio processing
  • Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
  • Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
  • Explore the process required to implement Autoencoders
  • Evolve a deep neural network using reinforcement learning

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Publication date : Apr 26, 2017
Length: 318 pages
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Table of Contents

9 Chapters
Neural Networks Foundations Chevron down icon Chevron up icon
Keras Installation and API Chevron down icon Chevron up icon
Deep Learning with ConvNets Chevron down icon Chevron up icon
Generative Adversarial Networks and WaveNet Chevron down icon Chevron up icon
Word Embeddings Chevron down icon Chevron up icon
Recurrent Neural Network — RNN Chevron down icon Chevron up icon
Additional Deep Learning Models Chevron down icon Chevron up icon
AI Game Playing Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(20 Ratings)
5 star 25%
4 star 20%
3 star 35%
2 star 15%
1 star 5%
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Kyle Mar 07, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Fast and convenient, no surprises.
Amazon Verified review Amazon
Amazon Customer Sep 07, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is definitely one of the best resources if you want to learn Keras.
Amazon Verified review Amazon
Arbaaz Qureshi Jun 23, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book, for readers who are familiar with deep learning concepts and Keras and wish to improve them further. Correct the errors of the code section, in the next version.
Amazon Verified review Amazon
Tae S. Shin Dec 09, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I needed a reference book to use Keras that is a user-oriented library for easy modeling of neural networks in Python. Unlike some low reviews on the book, it turned out to be exactly what I expected and what its title said, Implementing deep learning models and neural networks with Keras in Python.If you want to know more about theory of deep learning, you should refer to other deep learning books. If you want to know how Keras API internally works, you may want to look at other books on Tensorflow or Theano that was low level API for Keras and with which you can define neural networks in node-level. But if you want to flexibly and easily build a NN model with fewer lines of code, this book might be good for you.
Amazon Verified review Amazon
Melvin Dec 30, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I felt compelled to write a review because I really think this is an exceptionally good book under the circumstances. When I say "under the circumstances", I mean given the fact that deep learning is a challenging topic to explain and requires both a theoretic and practical approach to be appreciated. The author clearly avoids getting bogged down with the theoretical aspects and I can appreciate why since a thorough theoretical understanding would require a separate book in it's own right.This book will not help you understand the theory or underlying mathematics. However, if you already understand the theory and want to learn to use a package like Keras then this is the book for you.This book stands out because it gives details about the implementation aspects of coding many different deep learning models that you will hear about in the literature and in the field. For example, LeNet, ResNet, etc. among many others are demonstrated through out the book.Generally speaking, topics in deep learning are not easy to explain to the average reader and I think the author recognizes this difficulty and chooses to place his focus on demonstrating how to implement deep learning methods and being careful to explain what the different modules do and their respective parameters.In my view, this book is very suitable for Data Scientists who already know the spectrum of machine learning models and techniques and want to get their hands dirty as fast as possible with deep learning. This book is a much better practical book for deep learning than the popular book by Aurélien Géron called "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I have looked at many deep learning books and in my view this one did the best job is getting me comfortable with implementing deep learning models on my own.The one thing that I found the book was lacking is that it's final chapter on AI and reinforcement learning did not seem as thorough and detailed as the other chapters in the book. Having reviewed many books in the area of deep learning, I can honestly say this is probably the best book I have come across so far. However, I came to this book already having a solid understand of deep learning theory.
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