Book Image

Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara
Book Image

Neural Networks with Keras Cookbook

By: V Kishore Ayyadevara

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)

Building a chatbot

A chatbot is helpful in a scenario where the bot automates some of the more common queries. This is very useful in a practical scenario, especially in cases where you would have to just look up the result from a database or query an API to obtain the result that the query is about.

Given this, there are potentially two ways that you can design a chatbot, as follows:

  • Convert the unstructured user query into a structured format:
    • Query from the database based on the converted structure
  • Generate responses based on the input text

For this exercise, we will be adopting the first approach, as it is more likely to give predictions that can be tweaked further before presenting them to the user. Additionally, we will also understand the reason why we might not want to generate responses based on input text after we go through the machine translation and text summarization...