Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

History

The basics of continuous backpropagation were proposed by Henry J. Kelley [1] in 1960 using dynamic programming. Stuart Dreyfus proposed using the chain rule in 1962 [2]. Paul Werbos was the first proposing to use backpropagation for neural nets in his 1974 PhD Thesis [3]. However, it was only in 1986 that backpropagation gained success with the work of David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams published in Nature [4]. Only in 1987, Yan LeCun described the modern version of backpropagation currently used for training neural networks [5].

The basic intuition of SGD was introduced by Robbins and Monro in 1951 in a context different from neural networks [6]. Only in 2012 – or 52 years after the first time backpropagation was first introduced – AlexNet [7] achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge using GPUs. According to The Economist [8], "Suddenly people started to pay attention, not just within the AI community...