Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Translating sentences

Another of deep learning in text has got its boost in performance and popularity thanks to a couple of large search engines. Translating sentences is a hard task because each language has its own rules, exceptions, and expressions. What we tend to forget is that the translation of words often largely depends on the context around it. Some believe that solving a language thoroughly can be a huge milestone for achieving general AI, because intelligence and language are well connected and small nuances in language can be crucial to understanding.

In our next recipe, we will demonstrate how to use a sequence-to-sequence model to translate sentences from English to French. We will be using the CNTK framework. 

How to do it...

  1. Let's start with loading the libraries:
import numpy as np
import os

from cntk import Trainer, Axis
from import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs, INFINITELY_REPEAT
from cntk.learners import momentum_sgd, fsadagrad, momentum_as_time_constant_schedule...