Book Image

Intelligent Projects Using Python

By : Santanu Pattanayak
Book Image

Intelligent Projects Using Python

By: Santanu Pattanayak

Overview of this book

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Table of Contents (12 chapters)

Preprocessing the movie review text

The movie review text needs to be preprocessed and converted to numerical tokens, corresponding to different words in the corpus. The Keras tokenizer will be used to convert the words into numerical indices, or tokens, by taking the first 50000 frequent words. We have restricted the movie reviews to have a maximum of 1000 word tokens. If a movie review has less than 1000 word tokens, the review is padded with zeros at the beginning. After the preprocessing, the data is split into train, validation, and test sets. The Keras Tokenizer object is saved for use during inference.

The detailed code( for preprocessing the movie reviews is as follows:

# -*- coding: utf-8 -*-
Created on Sun Jun 17 22:36:00 2018
@author: santanu
import numpy as np
import pandas as pd
import os
import re
from keras.preprocessing...