The core logic of the Android app is to process the user requests, along with the data passed, and then send the results back to the user. As a part of this mobile app, the core logic will accept the movie review provided by the user, process the raw data, and convert it to a format that the trained LSTM model can run an inference on. The OnClickListener functionality in Java is utilized to monitor whether the user has submitted a processing request. Each of the words in the provided movie review needs to be changed to their indices, before the input can be fed directly to the optimized trained LSTM model for inference. Aside from the optimized protobuf model, a dictionary of the words and their corresponding indices is also stored for this purpose. The TensorFlowInferenceInterface methods are used to run inferences with the trained model. The...
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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