▶Book Description
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.
▶What You Will Learn
⦁ Build an intelligent machine translation system using seq-2-seq neural translation machines
⦁ Create AI applications using GAN and deploy smart mobile apps using TensorFlow
⦁ Translate videos into text using CNN and RNN
⦁ Implement smart AI Chatbots, and integrate and extend them in several domains
⦁ Create smart reinforcement, learning-based applications using Q-Learning
⦁ Break and generate CAPTCHA using Deep Learning and Adversarial Learning
▶Key Features
⦁ A go-to guide to help you master AI algorithms and concepts
⦁ 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance
⦁ Use TensorFlow, Keras, and other Python libraries to implement smart AI applications
▶Who This Book Is For
This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book
▶What this book covers
⦁ Chapter 1, Foundations of Artificial Intelligence Based Systems, covers the basics of how to build smart artificial systems using machine learning, deep learning, and reinforcement learning. We will be discussing various artificial neural networks, including CNNs for image processing purposes and RNNs for natural language processing purposes.
⦁ Chapter 2, Transfer Learning, covers how to use transfer learning to detect diabetic retinopathy conditions in the human eye, and to determine the retinopathy's severity. We will explore CNNs and learn how to train a model with CNN that is capable of detecting diabetic retinopathy in fundus images of the human eye.
⦁ Chapter 3, Neural Machine Translation, covers the basics of recurrent neural network (RNN) architectures. We will also learn about three different machine translation systems: rule-based machine translation, statistical machine translation, and neural machine translation.
⦁ Chapter 4, Style Transfer in Fashion Industry using GANs, explains how to create a smart AI model to generate shoes with a similar style to a given handbag and vice versa. We will be using the Vanilla GAN to implement the project using customized versions of the GAN, such as a DiscoGAN and a CycleGAN.
⦁ Chapter 5, Video Captioning Application, discusses the role of CNNs and LSTMs in video captioning and explains how to build a video captioning system leveraging the sequence to sequence—video to text architecture.
⦁ Chapter 6, The Intelligent Recommender System, discusses recommender systems, which are information filtering systems that deal with the problem of digital data overload to pull out items or information according. We will be using latent factorization for collaborative filtering and use a restricted Boltzmann machine to build recommendation systems.
⦁ Chapter 7, Mobile App for Movie Review Sentiment Analysis, explains how machine learning as a service is used to benefit mobile apps. We will be creating an Android mobile app using TensorFlow that will take reviews of movies as input and provide a rating based on sentiment analysis.
⦁ Chapter 8, Conversational AI Chatbots for Customer Service, explains how chatbots have evolved during and looks at the benefits of having conversational chatbots. We will also be looking into how to create chatbots and what LSTM sequence-to-sequence models are. We will also be building a sequence-to-sequence model for a Twitter support chatbot.
⦁ Chapter 9, Autonomous Self-Driving Car Through Reinforcement Learning, explains reinforcement learning and Q-learning. We will also be crating a self-driving car using deep learning and reinforcement learning.
⦁ Chapter 10, CAPTCHA from a Deep-Learning Perspective, we discusses what CAPTCHAs are and why they are needed. We will also be creating a model to break CAPTCHAs using deep learning and then how to generate them using adversarial learning.
