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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
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12
Index

Distributed computing


DL models have to be trained on a large amount of data to improve their performance. However, training a deep network with millions of parameters may take days, or even weeks. In Large Scale Distributed Deep Networks, Dean et al. proposed two paradigms, namely model parallelism and data parallelism, which allow us to train and serve a network model on multiple physical machines. In the following section, we introduce these paradigms with a focus on distributed TensorFlow capabilities.

Model parallelism

Model parallelism gives every processor the same data but applies a different model to it. If the network model is too big to fit into one machine's memory, different parts of the model can be assigned to different machines. A possible model parallelism approach is to have the first layer on a machine (node 1), the second layer on the second machine (node 2), and so on. Sometimes this is not the optimal approach, because the last layer has to wait for the first layer's...

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