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)

Text prediction

Language computational models based on RNNs are nowadays among the most successful techniques for statistical language modeling. They can be easily applied in a wide range of tasks, including automatic speech recognition and machine translation.

In this section, we'll explore an RNN model on a challenging task of language processing, guessing the next word in a sequence of text.

You'll find a complete reference for this example in the following page:
https://www.tensorflow.org/versions/r0.8/tutorials/recurrent/index.html.

You can download the source code for this example here (official TensorFlow project GitHub page):
https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb.

The files to download are as follows:

  • ptb_word_lm.py: This file contains code to train the model on the PTB dataset
  • reader.py: This file contains code to read the dataset

Here we just present only the main...