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Intelligent Projects Using Python

Intelligent Projects Using Python

By : Pattanayak
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Intelligent Projects Using Python

Intelligent Projects Using Python

5 (3)
By: 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)
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Learning to generate natural handbags from sketched outlines

In this chapter, we are going to generate handbags from sketched outlines without using explicit pair matching using DiscoGAN. We denote the sketch images as belonging to domain A, while the natural handbag images to belong to domain B. There will be two generators: one that takes the images of domain A and maps them to images that would look realistic under domain B, and another that does the opposite: one that maps handbag images from domain B to images that will look realistic under domain A. The discriminators would try to identify the generator generated fake images from those of the authentic images in each domain. The generators and the discriminator would play a minimax zero- sum game against each other.

To train this network, we will require two sets of images, sketches, or outlines of handbags and natural images...

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