We will be building an Android app that will take movie reviews as input and provide a rating from 0 to 5 as an output, based on a sentiment analysis of the movie review. An LSTM version of the recurrent neural network would first be trained to carry out a binary classification on the sentiment of the movie. The training data would consist of text-based movie reviews, along with a binary label of 0 or 1. A label of 1 stands for a review that has a positive sentiment, while 0 denotes that the movie has a negative sentiment. From the model, we will predict the probability of the sentiment being positive, and then scale up the probability by a factor of five, to convert it into a reasonable rating. The model will be built using TensorFlow, and then the trained model will be converted to an optimized frozen protobuf object, to be integrated with...
Intelligent Projects Using Python
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Intelligent Projects Using Python
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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.
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Intelligent Projects Using Python
Table of Contents (12 chapters)
Preface
Free Chapter
Foundations of Artificial Intelligence Based Systems
Transfer Learning
Neural Machine Translation
Style Transfer in Fashion Industry using GANs
Video Captioning Application
The Intelligent Recommender System
Mobile App for Movie Review Sentiment Analysis
Conversational AI Chatbots for Customer Service
Autonomous Self-Driving Car Through Reinforcement Learning
CAPTCHA from a Deep-Learning Perspective
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