During preprocessing, we trained a Keras tokenizer to replace the words with their numerical word indices, so that the processed movie reviews could be fed to the LSTM model for training. We have also kept the first 50000 words with the highest word frequency, and have set the review sequences to be of a maximum length of 1000. Although the trained Keras tokenizer was saved for inference, it cannot be used by the Android app directly. We can restore the Keras tokenizer and save the first 50000 words and their corresponding word indices in a text file. This text file can be used in the Android app, in order to build a word-to-indices dictionary to convert the words of the review text to their word indices. It is important to note that the word to indices mapping can be retrieved from the loaded Keras tokenizer object, by referring...
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.
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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