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)

Markov decision process

Any reinforcement learning problem can be viewed as a Markov decision process, which we briefly looked at in Chapter 1, Foundations of Artificial Intelligence Based Systems. We will look at this again in more detail for your benefit. In the Markov decision process, we have an agent interacting with an environment. At any given instance, the t agent is exposed to one of many states: (s(t) = s) ∈ S. Based on the agent's action (a(t) = a) ∈ A in the state s(t) the agent is presented with a new state (s(t+1) = s) ∈ S. Here, S denotes the set of all states the agent may be exposed to, while A denotes the possible actions the agent can partake in.

You may now wonder how an agent takes action. Should it be random or based on heuristics? Well, it depends how much the agent has interacted with the environment in question. In the...