How To Get Into Ai

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Download How To Get Into Ai PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get How To Get Into Ai book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

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

Paradigms Of Artificial Intelligence Programming

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Author : Peter Norvig
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28

Paradigms Of Artificial Intelligence Programming written by Peter Norvig and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.

Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

How To Get Into Ai

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Author : Virversity Online Courses
language : en
Publisher: eBookIt.com
Release Date : 2025-02-20

How To Get Into Ai written by Virversity Online Courses and has been published by eBookIt.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.

Embark on an exciting journey into the world of Artificial Intelligence with our comprehensive online course. Designed for beginners, this course will equip you with the foundational knowledge needed to understand and engage with AI technologies, opening doors to numerous career opportunities. Master the Fundamentals of Artificial Intelligence Gain a solid understanding of AI concepts and terminologies. Learn about the historical background and evolution of Artificial Intelligence. Discover the practical applications and potential of AI in various industries. Acquire skills to identify and analyze AI trends and developments. Introduction to AI: Understanding the Basics of Artificial Intelligence Artificial Intelligence is transforming industries and shaping the future of technology. This course begins with an introduction to the core concepts and definitions that form the basis of AI. You will explore the historical milestones that have led to the current advancements in AI, gaining an appreciation for its rapid evolution. Through engaging lectures and practical examples, you will learn about the diverse applications of AI, from robotics and natural language processing to image recognition and beyond. This course will also provide insights into how AI is being implemented across different sectors, including healthcare, finance, and automotive industries. By the end of this course, you will have developed the ability to critically assess AI trends and contribute to discussions about its future impact. You will be equipped with a foundational understanding that prepares you for more advanced AI studies or to enter AI-related fields. Upon completing this course, you will emerge with a newfound confidence in your ability to engage with AI technologies and a clearer vision of how AI can be leveraged to enhance personal and professional growth.

Artificial Intelligence With Python

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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27

Artificial Intelligence With Python written by Prateek Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Computers categories.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Blondie24

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Author : David B. Fogel
language : en
Publisher: Morgan Kaufmann
Release Date : 2002

Blondie24 written by David B. Fogel and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

This book explains how a computer, by replicating the processes of Darwinian evolution, taught itself to play checkers far better than its creators could have programmed it to play. Fogel (editor, IEEE Transactions on Evolutionary Computation) considers the implications for evolutionary computations and artificial intelligence. Diagrams illustrate the evolutionary and computational processes at work, and the course of various games of checkers. Annotation copyrighted by Book News, Inc., Portland, OR.

How To Complete And Survive A Doctoral Dissertation

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Author : David Sternberg
language : en
Publisher: Macmillan + ORM
Release Date : 2025-06-25

How To Complete And Survive A Doctoral Dissertation written by David Sternberg and has been published by Macmillan + ORM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-25 with Study Aids categories.

How to Complete and Survive a Doctoral Dissertation by David Sternberg Mastering these skills spells the difference between "A.B.D." and "Ph.D." -refuting the magnum opus myth -coping with the dissertation as obsession (magnificent or otherwise) -the fine art of selecting a topic -writing the dissertation with publication in mind -when to stand your ground and when to prudently retreat if the committee's conception of your thesis differs substantially from your own -dealing with obstructive committee members, and keeping the fences mended -how to reconsider "negative" findings as useful data -reviewing your progress, and getting out of the "dissertation dumps" -defending your paper successfully--distinguishing between mere formalities and a serious substantive challenge -exploiting the career potential of your dissertation -and much, much more

The Essence Of Artificial Intelligence

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Author :
language : en
Publisher: Pearson Education India
Release Date :

The Essence Of Artificial Intelligence written by and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Probabilistic Graphical Models

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Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009-07-31

Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Mathematics For Machine Learning

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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Ai And Machine Learning For Coders

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Author : Laurence Moroney
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-01

Ai And Machine Learning For Coders written by Laurence Moroney 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 2020-10-01 with Computers categories.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving