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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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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

PrettyTensor


PrettyTensor allows the developer to wrap TensorFlow operations to quickly chain any number of layers to define neural networks. Coming up is simple example of Pretty Tensor's capabilities: we wrap a standard TensorFlow object, pretty, into a library-compatible object; then we feed it through three fully connected layers, and we finally output a softmax distribution:

pretty = tf.placeholder([None, 784], tf.float32)
softmax = (prettytensor.wrap(examples)
 .fully_connected(256, tf.nn.relu)
 .fully_connected(128, tf.sigmoid)
 .fully_connected(64, tf.tanh)
 .softmax(10))

The PrettyTensor installation is very simple. You can just use the pip installer:

sudo pip install prettytensor

Chaining layers

PrettyTensor has three modes of operation that share the ability to chain methods.

Normal mode

In normal mode, every time a method is called, a new PrettyTensor is created. This allows easy chaining, and you can still use any particular object multiple times. This makes it easy to branch your...

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