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)

Statistical machine-learning systems

Statistical machine translation systems select a target text by maximizing its conditional probability, given the source text. For example, let's say we have a source text s and we want to derive the best equivalent text t in the target language. This can be derived as follows:

The formulation of P(t/s) in (1) can be expanded using Bayes' theorem as follows:

For a given source sentence, P(s) would be fixed, and, hence, finding the optimal target translation turns out to be as follows:

You may wonder why maximizing P(s/t)P(t) in place of P(t/s) directly would give an advantage. Generally, ill-formed sentences that are highly likely under P(t/s) are avoided by breaking the problem into two components, that is, P(s/t) and P(t), as shown in the previous formula:

Figure 3.2: Statistical machine translation architecture

As we can see...