Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

Why a computational graph?

Another key idea in TensorFlow is the deferred execution, during the building phase of the computational graph, you can compose very complex expressions (we say it is highly compositional), when you want to evaluate them through the running session phase, TensorFlow schedules the running in the most efficient manner (for example, parallel execution of independent parts of the code using the GPU).

In this way, a graph helps to distribute the computational load if one must deal with complex models containing a large number of nodes and layers.

Finally, a neural network can be compared to a composite function where each network layer can be represented as a function.

This consideration leads us to the next section, where the role of the computational graph in implementing a neural network is explained.

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