Author: Stuart Russell
Publisher: Createspace Independent Publishing Platform
ISBN: 9781537600314
Category :
Languages : en
Pages : 626
Book Description
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Human Compatible
Author: Stuart Jonathan Russell
Publisher: Penguin Books
ISBN: 0525558616
Category : Business & Economics
Languages : en
Pages : 354
Book Description
A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines.
Publisher: Penguin Books
ISBN: 0525558616
Category : Business & Economics
Languages : en
Pages : 354
Book Description
A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines.
Artificial Intelligence: A Modern Approach, Global Edition
Author: Stuart Russell
Publisher: Pearson Higher Ed
ISBN: 1292401176
Category :
Languages : en
Pages : 1167
Book Description
Explore the ever-expanding, fascinating field of Artificial Intelligence and its latest technologies with this industry-leading text. Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russel and Peter Norvigis the long-anticipated revision of this market-leading text, exploring the full breadth and depth of the field of Artificial Intelligence (AI). From robotic planetary explorers to online services with billions of users, the textbook covers a wide range of applications, delving into the advanced methods of reasoning, deep learning, perception and mathematics. Thoroughly updated and with new content, this latest edition brings you up to date on the latest technological advancements in the field, presenting concepts in a more unified manner. Some of the changes in the content include: Content that focuses deeper on machine learning rather than the hand-crafted knowledge of engineering. An updated, thorough discussion emphasises deep learning, probabilistic programming, and multi-agent systems. Extensive updates on the Robotics chapter now include content regarding the interaction of robots with humans. A new online site now includes all the exercises for this edition, allowing the team of authors to update and improve them continuously. Besides studying the methods and technologies, this edition also considers the ethical aspects and values of practicing the discipline. Fairness, integrity, respect, and social good, provide a fundamental framework to the learning process in this edition, studying the impact of AI on society. With a plethora of topics, exercises, and practical applications, this leading text is the must-read edition of this field, offering a deeper understanding and a multi-faceted approach to this expanding subject.
Publisher: Pearson Higher Ed
ISBN: 1292401176
Category :
Languages : en
Pages : 1167
Book Description
Explore the ever-expanding, fascinating field of Artificial Intelligence and its latest technologies with this industry-leading text. Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russel and Peter Norvigis the long-anticipated revision of this market-leading text, exploring the full breadth and depth of the field of Artificial Intelligence (AI). From robotic planetary explorers to online services with billions of users, the textbook covers a wide range of applications, delving into the advanced methods of reasoning, deep learning, perception and mathematics. Thoroughly updated and with new content, this latest edition brings you up to date on the latest technological advancements in the field, presenting concepts in a more unified manner. Some of the changes in the content include: Content that focuses deeper on machine learning rather than the hand-crafted knowledge of engineering. An updated, thorough discussion emphasises deep learning, probabilistic programming, and multi-agent systems. Extensive updates on the Robotics chapter now include content regarding the interaction of robots with humans. A new online site now includes all the exercises for this edition, allowing the team of authors to update and improve them continuously. Besides studying the methods and technologies, this edition also considers the ethical aspects and values of practicing the discipline. Fairness, integrity, respect, and social good, provide a fundamental framework to the learning process in this edition, studying the impact of AI on society. With a plethora of topics, exercises, and practical applications, this leading text is the must-read edition of this field, offering a deeper understanding and a multi-faceted approach to this expanding subject.
Conscious Mind, Resonant Brain
Author: Stephen Grossberg
Publisher: Oxford University Press
ISBN: 0190070552
Category : Computers
Languages : en
Pages : 771
Book Description
How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answers to these questions that modern theories of mind and brain have recently proposed. Stephen Grossberg is broadly acknowledged to be the most important pioneer and current research leader who has, for the past 50 years, modelled how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. This research has led to a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. The work embodies revolutionary Principia of Mind that clarify how autonomous adaptive intelligence is achieved. It provides mechanistic explanations of multiple mental disorders, including symptoms of Alzheimer's disease, autism, amnesia, and sleep disorders; biological bases of morality and religion, including why our brains are biased towards the good so that values are not purely relative; perplexing aspects of the human condition, including why many decisions are irrational and self-defeating despite evolution's selection of adaptive behaviors; and solutions to large-scale problems in machine learning, technology, and Artificial Intelligence that provide a blueprint for autonomously intelligent algorithms and robots. Because brains embody a universal developmental code, unifying insights also emerge about shared laws that are found in all living cellular tissues, from the most primitive to the most advanced, notably how the laws governing networks of interacting cells support developmental and learning processes in all species. The fundamental brain design principles of complementarity, uncertainty, and resonance that Grossberg has discovered also reflect laws of the physical world with which our brains ceaselessly interact, and which enable our brains to incrementally learn to understand those laws, thereby enabling humans to understand the world scientifically. Accessibly written, and lavishly illustrated, Conscious Mind/Resonant Brain is the magnum opus of one of the most influential scientists of the past 50 years, and will appeal to a broad readership across the sciences and humanities.
Publisher: Oxford University Press
ISBN: 0190070552
Category : Computers
Languages : en
Pages : 771
Book Description
How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answers to these questions that modern theories of mind and brain have recently proposed. Stephen Grossberg is broadly acknowledged to be the most important pioneer and current research leader who has, for the past 50 years, modelled how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. This research has led to a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. The work embodies revolutionary Principia of Mind that clarify how autonomous adaptive intelligence is achieved. It provides mechanistic explanations of multiple mental disorders, including symptoms of Alzheimer's disease, autism, amnesia, and sleep disorders; biological bases of morality and religion, including why our brains are biased towards the good so that values are not purely relative; perplexing aspects of the human condition, including why many decisions are irrational and self-defeating despite evolution's selection of adaptive behaviors; and solutions to large-scale problems in machine learning, technology, and Artificial Intelligence that provide a blueprint for autonomously intelligent algorithms and robots. Because brains embody a universal developmental code, unifying insights also emerge about shared laws that are found in all living cellular tissues, from the most primitive to the most advanced, notably how the laws governing networks of interacting cells support developmental and learning processes in all species. The fundamental brain design principles of complementarity, uncertainty, and resonance that Grossberg has discovered also reflect laws of the physical world with which our brains ceaselessly interact, and which enable our brains to incrementally learn to understand those laws, thereby enabling humans to understand the world scientifically. Accessibly written, and lavishly illustrated, Conscious Mind/Resonant Brain is the magnum opus of one of the most influential scientists of the past 50 years, and will appeal to a broad readership across the sciences and humanities.
ARTIFICIAL INTELLIGENCE: A MODERN APPROACH
Author: Dr. Anil Kumar
Publisher: Xoffencerpublication
ISBN: 8197370818
Category : Computers
Languages : en
Pages : 251
Book Description
Here we try to define artificial intelligence (AI) and explain why we think it deserves more attention than other worthy research topics; obviously, this is a prerequisite to doing any kind of study in this area. We humans take great pride in our intelligence; in fact, we call ourselves Homo sapiens, which means "man the wise." Human cognition has long baffled scientists, who have sought to explain how a little particle of stuff like us can see, understand, predict, and control an enormous and complex cosmos. Beyond that, the field of artificial intelligence (AI) aims to do more than just understand; it aims to build intelligent objects. One of the newest innovations in engineering and science is AI. The name wasn't even thought of until 1956, although development started in earnest almost immediately after WWII ended. Science professionals from several disciplines often mention artificial intelligence (AI) as the "field I would most like to be in" next to molecular biology. If you're a physics student, you could think that all the great thinkers like Galileo, Newton, Einstein, and others have thought of everything. Conversely, AI is still on the market for a handful of brilliant minds to join their team full-time. At now, AI encompasses a wide variety of subfields, from the broad (perception and learning) to the narrow (proving mathematical theorems, writing poetry, operating a car on a congested street, and disease detection, among many others). These are but a few of the many activities that might be categorised as AI-related. Artificial intelligence (AI) is a field that really covers all intellectual pursuits; it is relevant to everyone
Publisher: Xoffencerpublication
ISBN: 8197370818
Category : Computers
Languages : en
Pages : 251
Book Description
Here we try to define artificial intelligence (AI) and explain why we think it deserves more attention than other worthy research topics; obviously, this is a prerequisite to doing any kind of study in this area. We humans take great pride in our intelligence; in fact, we call ourselves Homo sapiens, which means "man the wise." Human cognition has long baffled scientists, who have sought to explain how a little particle of stuff like us can see, understand, predict, and control an enormous and complex cosmos. Beyond that, the field of artificial intelligence (AI) aims to do more than just understand; it aims to build intelligent objects. One of the newest innovations in engineering and science is AI. The name wasn't even thought of until 1956, although development started in earnest almost immediately after WWII ended. Science professionals from several disciplines often mention artificial intelligence (AI) as the "field I would most like to be in" next to molecular biology. If you're a physics student, you could think that all the great thinkers like Galileo, Newton, Einstein, and others have thought of everything. Conversely, AI is still on the market for a handful of brilliant minds to join their team full-time. At now, AI encompasses a wide variety of subfields, from the broad (perception and learning) to the narrow (proving mathematical theorems, writing poetry, operating a car on a congested street, and disease detection, among many others). These are but a few of the many activities that might be categorised as AI-related. Artificial intelligence (AI) is a field that really covers all intellectual pursuits; it is relevant to everyone
Deep Learning with PyTorch
Author: Luca Pietro Giovanni Antiga
Publisher: Simon and Schuster
ISBN: 1638354073
Category : Computers
Languages : en
Pages : 518
Book Description
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Publisher: Simon and Schuster
ISBN: 1638354073
Category : Computers
Languages : en
Pages : 518
Book Description
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
The Hundred-page Machine Learning Book
Author: Andriy Burkov
Publisher:
ISBN: 9781999579500
Category : Machine learning
Languages : en
Pages : 141
Book Description
Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.
Publisher:
ISBN: 9781999579500
Category : Machine learning
Languages : en
Pages : 141
Book Description
Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.