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)

Deep Q learning

Deep Q learning leverages deep learning networks in learning the Q value function. Illustrated in the following diagram, Figure 9.3, is the architecture of a deep Q learning network:

Figure 9.3: Illustration of a deep Q network

The diagram learns to map every pair of states (s, a) and actions into an output Q value output Q(s, a), while in the diagram on the right, for every state s, we learn Q values pertaining to every action a. If there are n possible actions for every state, the output of the network produces n outputs Q(s, a1), Q(s, a2), . . . . . . Q(s, an).

The deep Q learning networks are trained with a very simple idea called experience replay. Let the RL agent interact with the environment and store experience in the tuple form of (s, a, r, s) in a replay buffer. Mini-batches can be sampled from this replay buffer to train the network. In the...