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)

Learning the Q value function

For an RL agent to make a decision, it is important for the agent to learn the Q value function. The Q value function can be learned iteratively via Bellman's equation. When the agent starts to interact with the environment, it starts with a random state s(0) and random state of Q values for every state action pair. The agent's action would also be somewhat random, since it has no state Q values to make informed decisions. For each action taken, the environment would return a reward based on which agent starts to build the Q value tables, and improves over time.

At any exposed state s(t) at iteration t the agent would take an action a(t) that maximizes its long-term reward. The Q table holds the long-term reward values, and hence the chosen a(t) would be based on the following heuristics:

The Q value table is also indexed by iteration...