With this, we come to the end of the chapter. All the code related to this chapter can be located in the GitHub link found at: https://github.com/PacktPublishing/Intelligent-Projects-using-Python/tree/master/Chapter10. You will now have a fair idea about how deep learning can influence CAPTCHAs. At one end of the spectrum, we can see how easily CAPTCHAs can be solved by bots with deep-learning AI applications in them. However, at the other end, we see how deep learning can be used to leverage a given dataset and create new CAPTCHAs from random noise. You can extend the technicalities learned about generative adversarial networks in this chapter to build a smart CAPTCHA generation system, using deep learning. And now, we come to the end of this book. I hope that this journey through the nine practical artificial intelligence-based applications has been an enriching one...
Intelligent Projects Using Python
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Intelligent Projects Using Python
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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.
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Current Title:
Intelligent Projects Using Python
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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Customer Reviews