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This is the code repository for Deep Learning with TensorFlow - Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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 v1.7, 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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
import tensorflow as tf # Import TensorFlow
x = tf.constant(8) # X op
y = tf.constant(9) # Y op
z = tf.multiply(x, y) # New op Z
A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. • Software: Python 3.5.0, Pip, pandas, numpy, tensor fl ow, Matplotlib 2.1.1, IPython, Scipy 0.19.0, sklearn, seaborn, tffm, and many more • Step: Issue the following command on Terminal on Ubuntu: $ sudo pip3 install pandas numpy tensorflow sklearn seaborn tffm Nevertheless, installing guidelines are provided in the chapters.