November 2017
Intermediate to advanced
274 pages
6h 16m
English
Content preview from Neural Network Programming with TensorFlow
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What this book covers
Chapter 1, Maths for Neural Networks, covers the basics of algebra, probability, and optimization techniques for neural networks.
Chapter 2, Deep Feedforward Networks, explains the basics of perceptrons, neurons, and feedforward neural networks. You will also learn about various learning techniques and mainly the core learning algorithm called backpropagation.
Chapter 3, Optimization for Neural Networks, covers optimization techniques that are fundamental to neural network learning.
Chapter 4, Convolutional Neural Networks, discusses the CNN algorithm in detail. CNNs and their application to different data types will also be covered.
Chapter 5, Recurrent Neural Networks, covers the RNN algorithm in detail. RNNs and their ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.