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Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python
Publisher's This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 and featuring TensorFlow 2, the Keras API, CNNs, GANs, RNNs, NLP, and AutoML, has now been published.
Key FeaturesImplement various deep learning algorithms in Keras and see how deep learning can be used in gamesSee how various deep learning models and practical use-cases can be implemented using KerasA practical, hands-on guide with real-world examples to give you a strong foundation in KerasBook DescriptionThis 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.
What you will learnOptimize step-by-step functions on a large neural network using the Backpropagation algorithmFine-tune a neural network to improve the quality of resultsUse deep learning for image and audio processingUse Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special casesIdentify problems for which Recurrent Neural Network (RNN) solutions are suitableExplore the process required to implement AutoencodersEvolve a deep neural network using reinforcement learning
Key FeaturesImplement various deep learning algorithms in Keras and see how deep learning can be used in gamesSee how various deep learning models and practical use-cases can be implemented using KerasA practical, hands-on guide with real-world examples to give you a strong foundation in KerasBook DescriptionThis 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.
What you will learnOptimize step-by-step functions on a large neural network using the Backpropagation algorithmFine-tune a neural network to improve the quality of resultsUse deep learning for image and audio processingUse Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special casesIdentify problems for which Recurrent Neural Network (RNN) solutions are suitableExplore the process required to implement AutoencodersEvolve a deep neural network using reinforcement learning
320 pages, Kindle Edition
Published April 26, 2017
42 people are currently reading
92 people want to read
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Displaying 1 - 5 of 5 reviews
January 11, 2020
This book cleared lot of my concepts regarding deep learning.
A good read for anyone who is getting started in deep learning.
This book is more practical and less theoretical. If you want to deep dive into mathematics of deep learning, this book isn't for you.
I liked how the author explained the codes. Anyone can get up and ready building with such a book.
A good read for anyone who is getting started in deep learning.
This book is more practical and less theoretical. If you want to deep dive into mathematics of deep learning, this book isn't for you.
I liked how the author explained the codes. Anyone can get up and ready building with such a book.
February 12, 2020
Great Introduction to ML
Excellent introduction to Keras and machine learning. Ideal for newbies to this field. Great examples covering the breadth of current state of the discipline.
Excellent introduction to Keras and machine learning. Ideal for newbies to this field. Great examples covering the breadth of current state of the discipline.
June 17, 2017
very good practical overview of the Keras Framework using TensorFlow.
Was a little light on the algorithms.
Was a little light on the algorithms.
November 15, 2017
Code often doesn't work, text sometimes incomprehensible without referring to original papers. Otherwise great text, very up-to-date (summer 2017)
June 12, 2018
It is like Cooking art when you know all food components on molecular level.
Displaying 1 - 5 of 5 reviews