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Learn modern-day technologies from modern-day technical giants Key Features Real-world success and failure stories of artificial intelligence explained Understand concepts of artificial intelligence and deep learning methods Learn how to use artificial intelligence and deep learning methods Know how to prepare dataset and implement models using industry leading Python packages You’ll be able to apply and analyze the results produced by the models for prediction Description The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed. All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research. What You Will Learn How to use the algorithms written in the Python programming language to design models and perform predictions in general datasets Understand use cases in different industries related to the implementation of artificial intelligence and deep learning methods Learn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methods Who this book is for This book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods. Table of Contents 1. Artificial Intelligence and Deep Learning 2. Data Science for Business Analysis 3. Decision Making 4. Intelligent Computing Strategies By Google 5. Cognitive Learning Services in IBM Watson 6. Advancement web services by Baidu 7. Improved Social Business by Facebook 8. Personalized Intelligent Computing by Apple 9.Cloud Computing Intelligent by Microsoft About the Authors Dr. Jagreet Kaur is a doctorate in computer science and engineering. Her topic of thesis was “ARTIFICIAL INTELLIGENCE BASED ANALYTICAL PLATFORM FOR PREDICTIVE ANALYSIS IN HEALTH CARE.” With more than 12 years of experience in academics and research, she is working in data wrangling, machine learning and deep learning algorithms on large datasets, real-time data often in production environments for data science solutions and data products to get actionable insights for the last four years. Navdeep Singh Gill is a technology and solution architect having more than 15 years of experience in the IT and Telecom industry. For the past six years, he is working in big data analytics, automation and advanced analytics using machine learning and deep learning for planning and architecting of data science solutions and data products.
Artificial Intelligence, the Revolutionary Transformation that no one can scape Key Features Provides perfect ‘playground’ for enterprises and institutions globally to develop Artificial Intelligence solutions The world has achieved an enormous amount of technological advancement and skyrocketing progress in mass Digitization, Data Science, and FinTech The gist of the golden era of AI and FinTech AI-powered autonomous vehicles are undoubtedly the future. Autonomous vehicles are the dawn of a whole new lifestyle Using Artificial Intelligence to redefine their products, processes and strategies Description The book ‘Artificial Intelligence for All’ is a snapshot of AI applications in different industries, society, and everyday life. The book is written considering possibilities AI can bring in the Indian context and considering Indian industries and economy at the center stage. The book starts with describing the race for the supremacy of different countries in the field of Artificial Intelligence that has already taken a great momentum and how AI has managed to influence even mainstream politics and the world leaders. In the subsequent chapters, the book brings in AI applications primarily in the Banking and Finance sectors like Financial Crime detection using AI, Credit Risk Assessment, AI-powered conversational banking, Predictive Analytics, and recommendations in Banking and Finance. What will you learn This book is for both technical and non-technical readers, a cutting edge technology like Artificial Intelligence is simplified for all and a genuine effort has been made to democratize it as much as possible. The book will provide insights into the real applications of AI in different industries like health care and medicine, banking and finance, manufacturing, retail, sports, and many more, including how it’s transforming our life which probably many of us are not even aware of. And most importantly how a country like India can be benefitted by embracing this groundbreaking technology and the huge opportunities and economic impact that AI can bring. Who this book is for This book is useful for AI Professionals, Data Scientists... The content of the book is for both Technical and Non Technical readers who want to know the applications of AI in different industries. No prior technical or programming experience is required to understand this book. Table of Contents 1. Super Powers of AI – The Leaders and the Contenders 2. AI – The Core Fabric for NextGen Banking 3. How an AI Framework can be a Game-Changer in Your AI Journey 4. Artificial Neural Networks 5. The Next Wave of Automation will Transform our Living Experience 6. Self-Driving Cars – Socio-Economic Impact of Autonomous Vehicles 7. How Artificial Intelligence is Transforming the BFSI Sector 43 8. AI Now is a Race Among Startups and Tech Giants 9. AI in the top of priorities for CIOs and CTOs 10. AI in Sports 11. How a Country can be Transformed Using Artificial Intelligence 12. Don’t Underestimate the Power of an AI Chatbot 13. Industry Adoption of Cognitive and Artificial Intelligence 14. Artificial Intelligence – The Biggest Disruptor in the BFSI Industry 15. AI in Healthcare 16. AI in Cyber Security – Cognitive Cyber Defense 17. Be Aware of Cyber Threat 18. AI Revolution in India – National Strategy for AI 19. AI in Tour and Travels – Journey of a Digital Traveler 20. Top 100 Business Use Cases of Artificial Intelligence 21. T Impact of Modern Automation on Employment
A Practicing Guide to TensorFlow and Deep Learning Key Features -Equipped with a necessary introduction to Deep Learning and AI. -Includes demos and templates to give your projects a good start. -Find more on the most important facets of AI, image, and speech recognition. Description This book begins with the configuration of an Anaconda development environment, essential for practicing the deep learning process. The basics of machine learning, which are needed for Deep Learning, are explained in this book. TensorFlow is the industry-standard library for Deep Learning, and thereby, it is covered extensively with both versions, 1.x and 2.x. As neural networks are the heart of Deep Learning, the book explains them in great detail and systematic fashion, beginning with a single neuron and progressing through deep multilayer neural networks. The emphasis of this book is on the practical application of key concepts rather than going in-depth with them. After establishing a firm basis in TensorFlow and Neural Networks, the book explains the concepts of image recognition using Convolutional Neural Networks (CNN), followed by speech recognition, and natural language processing (NLP). Additionally, this book discusses Transformers, the most recent advancement in NLP. What you will learn -Create machine learning models for classification and regression. -Utilize TensorFlow 1.x to implement neural networks. -Work with the Keras API and TensorFlow 2. -Learn to design and train image categorization models. -Construct translation and Q & A apps using transformer-based language models. Who this book is for This book is intended for those new to Deep Learning who want to learn about its principles and applications. Before you begin, you'll need to be familiar with Python. Table of Contents 1. Introduction to Artificial Intelligence 2. Machine Learning 3. TensorFlow Programming 4. Neural Networks 5. TensorFlow 2 6. Image Recognition 7. Speech Recognition
Learn AI & Machine Learning from the first principles. Key Features Explore how different industries are using AI and ML for diverse use-cases. Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. Cutting-edge coverage on use of ML for business products and services. Explore how different companies are monetizing AI and ML technologies. Learn how you can start your own journey in the AI field from scratch. Description AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. What you will learn Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words How you can leverage and integrate AI technologies in your business to differentiate your product in the market. The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. How AI can help companies in launching new products, improving existing ones and automating mundane processes. Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. Who this book is for This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. Table of Contents 1.Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid About the Authors Prashant Kikani is an experienced data scientist, who ranks in the top 1% worldwide in competitions and kernels on Kaggle which is the world's largest community and platform for data science and machine learning. As part of his day-to-day work, he is working on solving some of the hardest problems for the human kind, like language translation using state-of-the-art deep learning-based NLP models and infusion of knowledge graphs in NLP models. He is one of the youngest students to achieve the Master title on the Kaggle kernel platform. Also, he has worked on other deep learning sub-fields like computer vision via Kaggle competitions. His interests lie in AI/ML and deep learning, and teaching others what he has learned in a very simple and intuitive manner. This book is part of his interest to share his knowledge in the simplest possible manner with everyone so that everyone they can learn about this fascinating technology called AI! Blog links:http://prashantkikani.com LinkedIn Profile://https://www.linkedin.com/in/prashant-kikani/
Master Computer Vision concepts using Deep Learning with easy-to-follow steps Key Features Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applications Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architectures Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook Description This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification.
Best Book on GAN Key Features Understanding the deep learning landscape and GAN’s relevance Learning basics of GAN Learning how to build GAN from scratch Understanding mathematics and limitations of GAN Understanding GAN applications for Retail, Healthcare, Telecom, Media and EduTech Understanding the important GAN papers such as pix2pixGAN, styleGAN, cycleGAN, DCGAN Learning how to build GAN code for industrial applications Understanding the difference between varieties of GAN Description This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially Generative Adversarial Networks(GAN). Industry professionals, coders, and data scientists can learn GAN from scratch. They can learn how to build GAN codes for industrial applications for Healthcare, Retail, HRTech, EduTech, Telecom, Media, and Entertainment. Mathematics of GAN is discussed and illustrated. KL divergence and other parts of GAN are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix GAN, DCGAN, CGAN, styleGAN, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book. What will you learn Machine Learning Researchers would be comfortable in building advanced deep learning codes for Industrial applications Data Scientists would start solving very complex problems in deep learning Students would be ready to join an industry with these skills Average data engineers and scientists would be able to develop complex GAN codes to solve the toughest problems in computer vision Who this book is for This book is perfect for machine learning engineers, data scientists, data engineers, deep learning professionals and computer vision researchers. This book is also very useful for medical imaging professionals, autonomous vehicles professionals, retail fashion professionals, media & entertainment professional, edutech and HRtech professionals. Professors and Students working in machine learning, deep learning, computer vision and industrial applications would find this book extremely useful. Table of Contents 1 Basics of GAN 2 Introduction 3 Problem with GAN 4 Famous Types Of GANs About the Author Navin K Manaswi has been developing AI solutions/products for HRTech, Retail, ITSM, Healthcare, Telecom, Insurance, Digital Marketing, and Supply Chain while working for Consulting companies in Malaysia, Singapore, and Dubai . He is a serial entrepreneur in Artificial Intelligence and Augmented Reality Space. He has been building solutions for video intelligence, document intelligence, and human-like chatbots. He is Guest Faculty at IIT Kharagpur for AI Course and an author of the famous book on deep learning. He is officially a Google Developer Expert in machine learning. He has been organizing and mentoring AI hackathons and boot camps at Google events and college events. His startup WoWExp has been building awesome products in AI and AR space. Your Blog links: www.navinmanaswi.com Your LinkedIn Profile: https://www.linkedin.com/in/navin-manaswi-1a708b8/
Learn RPA using Automation Anywhere with step-by-step practical implementation Key Features Get an overview of different stages in the Business Process Automation Learn how to use Automation Anywhere to automate business processes using commands such as Excel, Email, PDF, Database, XML, Web Services etc. Learn how to use commands together to automate process flows and standard industry use cases Learn how to develop bots in Bot Creator Learn to use Citrix AISense to capture objects in Citrix, Virtual Machine and Remote environment Description The book starts by giving an overview of Robotic Process Automation (RPA), its tools, and industry use cases. You will then get familiar with the Automation Anywhere Enterprise components and Architecture. Moving on, you will deep dive into the options provided in a Client application such as recorders, workbench, metabot designer and the types of bots in Automation Anywhere. You will then come across the practical implementation of variables in Automation. The book will then show how to implement commands such as Error Handling, XML, Web Services, FTP, OCR, PGP, String Operation, Files & Folders, etc. You will also get familiar with the working of Workflows and Workflow Manager. Towards the end, the book will teach you how to transfer bots to and from the Web Control Room and schedule bots from the Web Control Room. By the end of the book, you will be able to implement different commands provided in Automation Anywhere. What you will learn Understand the fundamentals of Business Process Automation and its stages. Use commands such as Excel, PDF, Email, Database, Object Cloning, Loops, If-Else etc. together to create a bot to automate industry use cases. Use Variables, MetaBots, IQ bots and Citrix AISense to incorporate features such as Reusability, Cognitive Automation capabilities and Object Capturing in Citrix, Virtual Machine and Remote environment. Learn how to create reusable bots using MetaBots Develop bots in Bot Creator and upload and schedule them in Web Control Room to be automatically executed on Bot Runner. Who this book is for The book is for anyone who wants to become a RPA developer. Professionals working in this field who want to upgrade themselves will find this book helpful. Table of Contents 1. Chapter 1: Automation Overview 2. Chapter 2: Introduction of RPA 3. Chapter 3: AAE Architecture 4. Chapter 4: Client Application 5. Chapter 5: Variables 6. Chapter 6: Use Cases 7. Chapter 7: Command Library 8. Chapter 8: Metabot 9. Chapter 9: Recorder 10. Chapter 10: Credential Variable 11. Chapter 11: IQ Bot 12. Chapter 12: Workflows 13. Chapter 13: System & Audit Logs 14. Chapter 14: Bot Transfer About the Authors Vaibhav Srivastava is a Software Delivery Manager with 11 years of experience in which he has implemented multiple assignments with varying roles like Architect, Business Analyst, Solution Consultant and Instructor for various technologies like RPA, Data Science, Machine Learning and .NET. Some of the organizations Vaibhav has been associated with are ContactPoint360, IBM, HPE, KPMG, Novartis, Unisys, TCL, HCL Technologies, Ryan India, NSEIT, to name a few. In 2017, Vaibhav moved into RPA and started his journey with Automation Anywhere. The journey has been a cherished one and has taken him on multiple unexplored areas. After Automation Anywhere, Vaibhav also developed expertise in UiPath, another market-leading RPA tool and later upgraded tech stack with gaining knowledge about Machine Learning, Data Science and Artificial Intelligence. Outside work, Vaibhav volunteers his spare time in helping, coaching, and mentoring young people in taking up careers in technology. LinkedIn Profile: https://www.linkedin.com/in/vaibhav-srivastava-44227113/