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)

Introduction to transfer learning

In a traditional machine learning paradigm (see Figure 2.1), every use case or task is modeled independently, based on the data at hand. In transfer learning, we use the knowledge gained from a particular task (in the form of architecture and model parameters) to solve a different (but related) task, as illustrated in the following diagram:

Figure 2.1: Traditional machine learning versus transfer learning

Andrew Ng, in his 2016 NIPS tutorial, stated that transfer learning would be the next big driver of machine learning's commercial success (after supervised learning); this statement grows truer with each passing day. Transfer learning is now used extensively in problems that need to be solved with artificial neural networks. The big question, therefore, is why this is the case.

Training an artificial neural network from scratch is a difficult...