Machine Learning Ai
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Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Artificial Intelligence In Practice
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Author : Bernard Marr
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-28
Artificial Intelligence In Practice written by Bernard Marr and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-28 with Business & Economics categories.
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.
Machine Learning For Kids
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Author : Dale Lane
language : en
Publisher: No Starch Press
Release Date : 2021-01-19
Machine Learning For Kids written by Dale Lane and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-19 with Computers categories.
A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+
Artificial Intelligence For Business
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Author : Doug Rose
language : en
Publisher:
Release Date : 2018-05-15
Artificial Intelligence For Business written by Doug Rose and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with categories.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
The Deep Learning Ai Playbook
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Author : Carlos Perez
language : en
Publisher: Lulu.com
Release Date : 2017
The Deep Learning Ai Playbook written by Carlos Perez and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Machine Learning And Artificial Intelligence
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Author : Ameet V Joshi
language : en
Publisher: Springer Nature
Release Date : 2019-09-24
Machine Learning And Artificial Intelligence written by Ameet V Joshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-24 with Technology & Engineering categories.
This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.
Artificial Intelligence Models Algorithms And Applications
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Author : Terje Solsvik Kristensen
language : en
Publisher: Bentham Science Publishers
Release Date : 2021-05-31
Artificial Intelligence Models Algorithms And Applications written by Terje Solsvik Kristensen and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-31 with Computers categories.
Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems.
Ai Machine Learning Inference Explained A Beginner S Guide
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Author : M.B. Chatfield
language : en
Publisher:
Release Date :
Ai Machine Learning Inference Explained A Beginner S Guide written by M.B. Chatfield and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unleash the Power of AI with Machine Learning Inference In today's data-driven world, artificial intelligence (AI) is rapidly transforming industries and reshaping our lives. At the heart of this revolution lies machine learning, which empowers computers to learn from vast amounts of data and make intelligent decisions without explicit programming. AI/Machine Learning Inference Explained: A Beginner's Guide is your comprehensive guide to understanding and implementing inference, the crucial process of applying machine learning models to real-world problems. Through clear explanations you'll gain a solid foundation in the principles and practices of inference, enabling you to: Grasp the fundamental concepts of machine learning and AI Discover the different types of machine learning models Understand the role of inference in machine learning Learn various inference techniques, including classification, regression, and anomaly detection Apply inference to solve real-world problems in various domains Whether you're a tech enthusiast, a data analyst, or a budding AI professional, this book will equip you with the knowledge and skills you need to harness the power of machine learning inference and make a meaningful impact in the AI landscape. Embrace the future of AI: Start learning machine learning inference today! #AIinference #MachineLearning #AI #FutureofTechnology #DataScience #ArtificialIntelligence #MachineLearningBook #AIbook #AIforBeginners #PredictiveAnalytics #AIInnovation #BigData #DeepLearning #TechTrends #DataDriven #LearnAI #AIApplications
Learn Ai With Python
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Author : Gaurav Leekha
language : en
Publisher: BPB Publications
Release Date : 2021-10-19
Learn Ai With Python written by Gaurav Leekha and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-19 with Computers categories.
Build AI applications using Python to intelligently interact with the world around you. KEY FEATURES ● Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. ● Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. ● Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. DESCRIPTION The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. WHAT YOU WILL LEARN ● Learn to implement various machine learning and deep learning algorithms to achieve smart results. ● Understand how ML algorithms can be applied to real-life applications. ● Explore logic programming and learn how to use it practically to solve real-life problems. ● Learn to develop different types of artificial neural networks with Python. ● Understand reinforcement learning and how to build an environment and agents using Python. ● Work with NLTK and build an automatic speech recognition system. WHO THIS BOOK IS FOR This book is for anyone interested in learning about artificial intelligence and putting it into practice with Python. This book is also valuable for intermediate Machine Learning practitioners as a reference guide. Readers should be familiar with the fundamental understanding of Python programming and machine learning techniques. TABLE OF CONTENTS 1. Introduction to AI and Python 2. Machine Learning and Its Algorithms 3. Classification and Regression Using Supervised Learning 4. Clustering Using Unsupervised Learning 5. Solving Problems with Logic Programming 6. Natural Language Processing with Python 7. Implementing Speech Recognition with Python 8. Implementing Artificial Neural Network (ANN) with Python 9. Implementing Reinforcement Learning with Python 10. Implementing Deep Learning and Convolutional Neural Network