The term CAPTCHA is an acronym for completely automated public Turing test to tell computers and humans apart. This is a computer program designed to distinguish between a human user and a machine or a bot, typically as a security measure to prevent spam and data misuse. The concept of CAPTCHA was introduced as early as 1997, when the internet search company AltaVista was trying to block automatic URL submissions to the platform that were skewing their search engine algorithms. To tackle this problem, AltaVista's chief scientist, Andrei Broder, came up with an algorithm to randomly generate images of text that could easily be identified by humans, but not by bots. Later, in 2003, Luis von Ahn, Manuel Blum, Nicholas J Hopper, and John Langford perfected this technology and called it CAPTCHA. The most common form of CAPTCHA requires...
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Book Overview & Buying
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Table Of Contents
Intelligent Projects Using Python
By :
Intelligent Projects Using Python
By:
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)
Preface
Free Chapter
Foundations of Artificial Intelligence Based Systems
Transfer Learning
Neural Machine Translation
Style Transfer in Fashion Industry using GANs
Video Captioning Application
The Intelligent Recommender System
Mobile App for Movie Review Sentiment Analysis
Conversational AI Chatbots for Customer Service
Autonomous Self-Driving Car Through Reinforcement Learning
CAPTCHA from a Deep-Learning Perspective
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