Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
Book Image

Hands-On Python Natural Language Processing

By: Aman Kedia, Mayank Rasu

Overview of this book

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
Table of Contents (16 chapters)
1
Section 1: Introduction
4
Section 2: Natural Language Representation and Mathematics
9
Section 3: NLP and Learning

How does a neural network learn?

The following steps represent step-by-step description of how information goes forward in a neural network. This process is referred to as forward propagation:

  1. The input values arrive at the input layer and are processed in the neurons.
  2. The outputs are then forwarded to the hidden layers wherein the randomly initialized weights are multiplied by the values and the bias is added.
  3. These values are then passed through the activation function.
  4. Finally, the values reach the output layer and the neurons perform the processing and emit an output value, y'.
  5. This y' is the predicted value for the input that came in.

Everything that we have discussed hitherto falls under the category of forward propagation.

As we saw, a value y' was predicted by the network. No learning has happened yet.

Now we need to judge the performance of our network, in terms of how far away or close it was to predicting the correct value. We do this by measuring something...