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Python Deep Learning

Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants
4.1 (10)
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Python Deep Learning

Python Deep Learning

4.1 (10)
By: Zocca, Spacagna, Daniel Slater, Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (12 chapters)
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11
Index

Training

Training a network means having already designed its topology. For that purpose we recommend the corresponding Auto-Encoder section in Chapter 4, Unsupervised Feature Learning for design guidelines according to the type of input data and expected use cases.

Once we have defined the topology of the neural network, we are just at the starting point. The model now needs to be fitted during the training phase. We will see a few techniques for scaling and accelerating the learning of our training algorithm that are very suitable for production environments with large datasets.

Weights initialization

The final convergence of neural networks can be strongly influenced by the initial weights. Depending on which activation function we have selected, we would like to have a gradient with a steep slope in the first iterations so that the gradient descent algorithm can quickly jump into the optimum area.

For a hidden unit j in the first layer (directly connected to the input layer), the sum of...

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