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

Analyzing sentiment

In an age where more and more is generated, and especially where every individual can post his or her opinion on the internet, the value of automatically analyzing these posts with high accuracy on a large scale is important for businesses and politics. In Chapter 4, Recurrent and Recursive Neural Networks, we've already shown how to apply RNNs with LSTM units to classify short sentences, such as movie reviews. In the following recipe, we will increase the complexity by classifying the sentiments of Twitter messages. We do this by predicting both binary classes and fine-grained classes.

How to do it...

  1. We start by all the libraries as follows:
from nltk.tokenize import word_tokenize 
from nltk.stem import WordNetLemmatizer 
import numpy as np 
import random 
import pickle 
from collections import Counter 

import tensorflow as tf
  1. Next, we process the English sentences with the nltk package. We start by defining the functions we need for preprocessing:


lemmatizer = WordNetLemmatizer...