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)

App interface page design

A simple mobile app interface can be designed using Android Studio, and the relevant code will be generated as an XML file. As you can see in the following screenshot (Figure 7.3), the app consists of a simple movie review textbox, where the users can input their movie reviews, and, once done, press the SUBMIT button. Once the SUBMIT button is pressed, the review will be passed to the core app logic, which will process the movie review text and pass it to the TensorFlow optimized model for inference.

As a part of the inference, a sentiment score will be computed, which will be displayed on the mobile app and also showcased as a star rating:

Figure 7.3: Mobile app user interface page format

The XML file required to generate the previously mentioned view of the mobile app is illustrated as follows:

<?xml version="1.0" encoding="utf-8...