The Science of Artificial Intelligence - Part 2 - Mastering the Qualitative Learning Surface

The Science of Artificial Intelligence - Part 2 - Mastering the Qualitative Learning Surface PDF Author: Michael Sinyangwe
Publisher:
ISBN: 9781090993847
Category :
Languages : en
Pages : 30

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Book Description
This book offers a framework for state-of-the-art machine learning techniques, which have been applied to the domain of sensual learning, thereby allowing AI systems to perceive the short-, medium-, and long-term configurations of their local environments. The focus is on machine image object recognition algorithms.

The Science of Artificial Intelligence - Part 2 - Mastering the Qualitative Learning Surface

The Science of Artificial Intelligence - Part 2 - Mastering the Qualitative Learning Surface PDF Author: Michael Sinyangwe
Publisher:
ISBN: 9781090993847
Category :
Languages : en
Pages : 30

Get Book Here

Book Description
This book offers a framework for state-of-the-art machine learning techniques, which have been applied to the domain of sensual learning, thereby allowing AI systems to perceive the short-, medium-, and long-term configurations of their local environments. The focus is on machine image object recognition algorithms.

The Sciences of the Artificial, reissue of the third edition with a new introduction by John Laird

The Sciences of the Artificial, reissue of the third edition with a new introduction by John Laird PDF Author: Herbert A. Simon
Publisher: MIT Press
ISBN: 0262537532
Category : Computers
Languages : en
Pages : 256

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Book Description
Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience.

Data Science and Machine Learning

Data Science and Machine Learning PDF Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538

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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Qualitative Research from Start to Finish, First Edition

Qualitative Research from Start to Finish, First Edition PDF Author: Robert K. Yin
Publisher: Guilford Press
ISBN: 1606239783
Category : Education
Languages : en
Pages : 369

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Book Description
This lively, practical text presents a fresh and comprehensive approach to doing qualitative research. The book offers a unique balance of theory and clear-cut choices for customizing every phase of a qualitative study. A scholarly mix of classic and contemporary studies from multiple disciplines provides compelling, field-based examples of the full range of qualitative approaches. Readers learn about adaptive ways of designing studies, collecting data, analyzing data, and reporting findings. Key aspects of the researcher's craft are addressed, such as fieldwork options, the five phases of data analysis (with and without using computer-based software), and how to incorporate the researcher's “declarative” and “reflective” selves into a final report. Ideal for graduate-level courses, the text includes:* Discussions of ethnography, grounded theory, phenomenology, feminist research, and other approaches.* Instructions for creating a study bank to get a new study started.* End-of-chapter exercises and a semester-long, field-based project.* Quick study boxes, research vignettes, sample studies, and a glossary.* Previews for sections within chapters, and chapter recaps.* Discussion of the place of qualitative research among other social science methods, including mixed methods research.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare PDF Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385

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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery PDF Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425

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Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

The Coding Manual for Qualitative Researchers

The Coding Manual for Qualitative Researchers PDF Author: Johnny Saldana
Publisher: SAGE
ISBN: 1446200124
Category : Reference
Languages : en
Pages : 282

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Book Description
The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example.

Interpretable Machine Learning

Interpretable Machine Learning PDF Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320

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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Resources in Education

Resources in Education PDF Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 372

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Book Description


Deep Learning for Coders with fastai and PyTorch

Deep Learning for Coders with fastai and PyTorch PDF Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624

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Book Description
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