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| lisp machine learning: Paradigms of Artificial Intelligence Programming Peter Norvig, 2014-06-28 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. |
| lisp machine learning: Practical Common Lisp Peter Seibel, 2006-11-01 Lisp is often thought of as an academic language, but it need not be. This is the first book that introduces Lisp as a language for the real world. Practical Common Lisp presents a thorough introduction to Common Lisp, providing you with an overall understanding of the language features and how they work. Over a third of the book is devoted to practical examples, such as the core of a spam filter and a web application for browsing MP3s and streaming them via the Shoutcast protocol to any standard MP3 client software (e.g., iTunes, XMMS, or WinAmp). In other practical chapters, author Peter Seibel demonstrates how to build a simple but flexible in-memory database, how to parse binary files, and how to build a unit test framework in 26 lines of code. |
| lisp machine learning: Clojure for Machine Learning Akhil Wali, 2014-04 A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated. This book is intended for Clojure developers who want to explore the area of machine learning. Basic understanding of the Clojure programming language is required, but thorough acquaintance with the standard Clojure library or any libraries are not required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage. |
| lisp machine learning: Artificial Intelligence with Common Lisp James L. Noyes, 1992 [The book] provides a balanced survey of the fundamentals of artificial intelligence, emphasizing the relationship between symbolic and numeric processing. The text is structured around an innovative, interactive combination of LISP programming and AI; it uses the constructs of the programming language to help readers understand the array of artificial intelligence concepts presented. After an overview of the field of artificial intelligence, the text presents the fundamentals of LISP, explaining the language's features in more detail than any other AI text. Common Lisp is then used consistently, in both programming exercises and plentiful examples of actual AI code.- Back cover This text is intended to provide an introduction to both AI and LISp for those having a background in computer science and mathematics. -Pref. |
| lisp machine learning: Artificial Intelligence Research and Development Beatriz López, 2005 The field covered by Artificial Intelligence (AI) is multiform and gathers subjects as various as the engineering of knowledge, the automatic treatment of the language, the training and the systems multiagents, and more. This book focuses on subjects including Machine Learning, Reasoning, Neural Networks, Computer Vision, and Multiagent Systems. |
| lisp machine learning: Common LISP David S. Touretzky, 2014-02-20 Highly accessible treatment covers cons cell structures, evaluation rules, programs as data, recursive and applicable programming styles. Nearly 400 illustrations, answers to exercises, toolkit sections, and a variety of complete programs. 1990 edition. |
| lisp machine learning: On Lisp Paul Graham, 1994 Written by a Lisp expert, this is the most comprehensive tutorial on the advanced features of Lisp for experienced programmers. It shows how to program in the bottom-up style that is ideal for Lisp programming, and includes a unique, practical collection of Lisp programming techniques that shows how to take advantage of the language's design for efficient programming in a wide variety of applications. |
| lisp machine learning: Common LISP Guy Steele, 1990-06-15 The defacto standard - a must-have for all LISP programmers. In this greatly expanded edition of the defacto standard, you'll learn about the nearly 200 changes already made since original publication - and find out about gray areas likely to be revised later. Written by the Vice- Chairman of X3J13 (the ANSI committee responsible for the standardization of Common Lisp) and co-developer of the language itself, the new edition contains the entire text of the first edition plus six completely new chapters. They cover: - CLOS, the Common Lisp Object System, with new features to support function overloading and object-oriented programming, plus complete technical specifications * Loops, a powerful control structure for multiple variables * Conditions, a generalization of the error signaling mechanism * Series and generators * Plus other subjects not part of the ANSI standards but of interest to professional programmers. Throughout, you'll find fresh examples, additional clarifications, warnings, and tips - all presented with the author's customary vigor and wit. |
| lisp machine learning: AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java George F. Luger, William A. Stubblefield, 2009 |
| lisp machine learning: Lisp Lore: A Guide to Programming the Lisp Machine H. Bromley, 2013-03-14 This book had its genesis in the following piece of computer mail: From allegra!joan-b Tue Dec 18 09:15:54 1984 To: sola!hjb Subject: lispm Hank, I've been talking with Mark Plotnik and Bill Gale about asking you to conduct a basic course on using the lisp machine. Mark, for instance, would really like to cover basics like the flavor system, etc., so he could start doing his own programming without a lot of trial and error, and Bill and I would be interested in this, too. I'm quite sure that Mark Jones, Bruce, Eric and Van would also be really interested. Would you like to do it? Bill has let me know that if you'd care to set something up, he's free to meet with us anytime this week or next (although I'll only be here on Wed. next week) so we can come up with a plan. What do you think? Joan. |
| lisp machine learning: Performance and Evaluation of LISP Systems Richard P. Gabriel, 1985-07-01 This final report of the Stanford Lisp Performance Study describes implementation techniques, performance tradeoffs, benchmarking techniques, and performance results for all of the major Lisp dialects in use today. |
| lisp machine learning: Lisp in Small Pieces Christian Queinnec, 2003-12-04 This will become the new standard reference for people wanting to know about the Lisp family of languages. |
| lisp machine learning: LISP 1.5 Programmer's Manual John McCarthy, Paul W. Abrahams, Daniel J. Edwards, Timothy P. Hart, Michael I. Levin, 1962-08-15 The manual describes LISP, a formal mathematical language. LISP differs from most programming languages in three important ways. The first way is in the nature of the data. The LISP language is designed primarily for symbolic data processing used for symbolic calculations in differential and integral calculus, electrical circuit theory, mathematical logic, game playing, and other fields of artificial intelligence. The manual describes LISP, a formal mathematical language. LISP differs from most programming languages in three important ways. The first way is in the nature of the data. In the LISP language, all data are in the form of symbolic expressions usually referred to as S-expressions, of indefinite length, and which have a branching tree-type of structure, so that significant subexpressions can be readily isolated. In the LISP system, the bulk of the available memory is used for storing S-expressions in the form of list structures. The second distinction is that the LISP language is the source language itself which specifies in what way the S-expressions are to be processed. Third, LISP can interpret and execute programs written in the form of S-expressions. Thus, like machine language, and unlike most other high level languages, it can be used to generate programs for further executions. |
| lisp machine learning: Practical FP in Scala (hard-Cover) Gabriel Volpe, 2020-03-25 Practical FP in Scala: A hands-on approach, is a book for intermediate to advanced Scala developers. Aimed at those who understand functional effects, referential transparency and the benefits of functional programming to some extent but who are missing some pieces to put all these concepts together to build a large application in a time-constrained manner.Throughout the chapters we will design, architect and develop a complete stateful application serving an API via HTTP, accessing a database and dealing with cached data, using the best practices and best functional libraries available in the Cats ecosystem.You will also learn about common design patterns such as managing state, error handling and anti-patterns, all accompanied by clear examples. Furthermore, at the end of the book, we will dive into some advanced concepts such as MTL, Classy Optics and Typeclass derivation. |
| lisp machine learning: Python Machine Learning Projects Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan, 2019-05-02 As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. |
| lisp machine learning: Let Over Lambda Doug Hoyte, 2008 Let Over Lambda is one of the most hardcore computer programming books out there. Starting with the fundamentals, it describes the most advanced features of the most advanced language: Common Lisp. Only the top percentile of programmers use lisp and if you can understand this book you are in the top percentile of lisp programmers. If you are looking for a dry coding manual that re-hashes common-sense techniques in whatever langue du jour, this book is not for you. This book is about pushing the boundaries of what we know about programming. While this book teaches useful skills that can help solve your programming problems today and now, it has also been designed to be entertaining and inspiring. If you have ever wondered what lisp or even programming itself is really about, this is the book you have been looking for. |
| lisp machine learning: The LISP Network Dino Farinacci, Victor Moreno, 2019-01-29 The complete guide to seamless anytime/anywhere networking with LISP In an era of ubiquitous clouds, virtualization, mobility, and the Internet of Things, information and resources must be accessible anytime, from anywhere. Connectivity to devices and workloads must be seamless even when people move, and their location must be fully independent of device identity. The Locator/ID Separation Protocol (LISP) makes all this possible. The LISP Network is the first comprehensive, in-depth guide to LISP concepts, architecture, techniques, behavior, and applications. Co-authored by LISP co-creator Dino Farinacci and Victor Moreno–co-developer of the Cisco LISP implementation–it will help you identify the opportunities and benefits of deploying LISP in any data center, campus and branch access, WAN edge, or service provider core network. This largely implementation-agnostic guide will be valuable to architects, engineers, consultants, technical sales professionals, and senior IT professionals in any largescale network environment. The authors show how LISP overcomes key problems in large-scale networking, thoroughly introduce its key applications, guide you through designing real-world solutions, and present detailed deployment case studies based on their pioneering experience. · Understand LISP’s core principles, history, motivation, and applications · Explore LISP’s technical architecture, components, mechanisms, and workflows · Use LISP to seamlessly deliver diverse network services and enable major advances in data center connectivity · Improve mobility, network segmentation, and policy management · Leverage software-defined WANs (SD-WANs) to efficiently move traffic from access to data center · Evolve access networks to provide pervasive, mega-scale, high-density modern connectivity · Integrate comprehensive security into the networking control and data plane, and learn how LISP infrastructure is protected against attacks · Enforce access control policies, connection integrity, confidentiality for data in flight, and end-point anonymity · Discover how LISP mobility mechanisms anticipate tomorrow’s application use cases |
| lisp machine learning: Common LISP Stuart Charles Shapiro, 1992 The text uses a tutorial style that focuses on learning by interaction and experimentation. |
| lisp machine learning: Programming in SCHEME Mark Watson, 2012-12-06 Scheme provides a flexible and powerful language for programming embodying many of the best features of logical and functional programming. This enjoyable book provides readers with an introduction to programming in Scheme by constructing a series of interesting and re-usable programs. The book includes two diskettes containing MIT Scheme to run on Windows PCs. |
| lisp machine learning: Common LISP Modules Mark Watson, 2012-12-06 While creativity plays an important role in the advancement of computer science, great ideas are built on a foundation of practical experience and knowledge. This book presents programming techniques which will be useful in both AI projects and more conventional software engineering endeavors. My primary goal is to enter tain, to introduce new technologies and to provide reusable software modules for the computer programmer who enjoys using programs as models for solutions to hard and interesting problems. If this book succeeds in entertaining, then it will certainly also educate. I selected the example application areas covered here for their difficulty and have provided both program examples for specific applications and (I hope) the method ology and spirit required to master problems for which there is no obvious solution. I developed the example programs on a Macintosh TM using the Macintosh Common LISP TM development system capturing screen images while the example programs were executing. To ensure portability to all Common LISP environments, I have provided a portable graphics library in Chapter 2. All programs in this book are copyrighted by Mark Watson. They can be freely used in any free or commercial software systems if the following notice appears in the fine print of the program's documentation: This program contains software written by Mark Watson. No royalties are required. The program miniatures contained in this book may not be distributed by posting in source code form on public information networks, or in printed form without my written permission. |
| lisp machine learning: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| lisp machine learning: Successful Lisp: How to Understand and Use Common Lisp David B. Lamkins, 2005 |
| lisp machine learning: The Essentials of Machine Learning and Neural Networks Dr.R.Gopinath, Mr.R.Vijay Sai, Mr.P.Sathishkumar, Dr.A.Gnanabaskaran, 2024-07-18 Dr.R.Gopinath, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Mr.R.Vijay Sai, Assistant Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Mr.P.Sathishkumar, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. Dr.A.Gnanabaskaran, Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology(Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India. |
| lisp machine learning: Learning GNU Emacs Debra Cameron, Bill Rosenblatt, Eric S. Raymond, 1996 Describes all of the new features of GNU Emacs 19.30, including fonts and colors, pull-down menus, scrollbars, enhanced X Window System support, and correct bindings for most standard keys. Gnus, a Usenet newsreader, and ange-ftp mode, a transparent interface to the file transfer protocol, are also described. |
| lisp machine learning: Common Lisp Recipes Edmund Weitz, 2016-01-01 Find solutions to problems and answers to questions you are likely to encounter when writing real-world applications in Common Lisp. This book covers areas as diverse as web programming, databases, graphical user interfaces, integration with other programming languages, multi-threading, and mobile devices as well as debugging techniques and optimization, to name just a few. Written by an author who has used Common Lisp in many successful commercial projects over more than a decade, Common Lisp Recipes is also the first Common Lisp book to tackle such advanced topics as environment access, logical pathnames, Gray streams, delivery of executables, pretty printing, setf expansions, or changing the syntax of Common Lisp. The book is organized around specific problems or questions each followed by ready-to-use example solutions and clear explanations of the concepts involved, plus pointers to alternatives and more information. Each recipe can be read independently of the others and thus the book will earn a special place on your bookshelf as a reference work you always want to have within reach. Common Lisp Recipes is aimed at programmers who are already familiar with Common Lisp to a certain extent but do not yet have the experience you typically only get from years of hacking in a specific computer language. It is written in a style that mixes hands-on no-frills pragmatism with precise information and prudent mentorship. If you feel attracted to Common Lisp's mix of breathtaking features and down-to-earth utilitarianism, you'll also like this book. |
| lisp machine learning: The Joy of Clojure Chris Houser, Michael Fogus, 2014-05-28 Summary The Joy of Clojure, Second Edition is a deep look at the Clojure language. Fully updated for Clojure 1.6, this new edition goes beyond just syntax to show you the why of Clojure and how to write fluent Clojure code. You'll learn functional and declarative approaches to programming and will master the techniques that make Clojure so elegant and efficient. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The Clojure programming language is a dialect of Lisp that runs on the Java Virtual Machine and JavaScript runtimes. It is a functional programming language that offers great performance, expressive power, and stability by design. It gives you built-in concurrency and the predictable precision of immutable and persistent data structures. And it's really, really fast. The instant you see long blocks of Java or Ruby dissolve into a few lines of Clojure, you'll know why the authors of this book call it a joyful language. It's no wonder that enterprises like Staples are betting their infrastructure on Clojure. About the Book The Joy of Clojure, Second Edition is a deep account of the Clojure language. Fully updated for Clojure 1.6, this new edition goes beyond the syntax to show you how to write fluent Clojure code. You'll learn functional and declarative approaches to programming and will master techniques that make Clojure elegant and efficient. The book shows you how to solve hard problems related to concurrency, interoperability, and performance, and how great it can be to think in the Clojure way. Appropriate for readers with some experience using Clojure or common Lisp. What's Inside Build web apps using ClojureScript Master functional programming techniques Simplify concurrency Covers Clojure 1.6 About the Authors Michael Fogus and Chris Houser are contributors to the Clojure and ClojureScript programming languages and the authors of various Clojure libraries and language features. Table of Contents PART 1 FOUNDATIONS Clojure philosophy Drinking from the Clojure fire hose Dipping your toes in the pool PART 2 DATA TYPES On scalars Collection types PART 3 FUNCTIONAL PROGRAMMING Being lazy and set in your ways Functional programming PART 4 LARGE-SCALE DESIGN Macros Combining data and code Mutation and concurrency Parallelism PART 5 HOST SYMBIOSIS Java.next Why ClojureScript? PART 6 TANGENTIAL CONSIDERATIONS Data-oriented programming Performance Thinking programs Clojure changes the way you think |
| lisp machine learning: Artificial Intelligence Stuart Jonathan Russell, Peter Norvig, 2013-07-31 In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises. |
| lisp machine learning: Dive Into Deep Learning Joanne Quinn, Joanne McEachen, Michael Fullan, Mag Gardner, Max Drummy, 2019-07-15 The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself. |
| lisp machine learning: Thinking in Java Bruce Eckel, 2003 Provides link to sites where book in zip file can be downloaded. |
| lisp machine learning: Funding a Revolution Committee on Innovations in Computing and Communications: Lessons from History, Computer Science and Telecommunications Board, National Research Council, 1999-01-25 The past 50 years have witnessed a revolution in computing and related communications technologies. The contributions of industry and university researchers to this revolution are manifest; less widely recognized is the major role the federal government played in launching the computing revolution and sustaining its momentum. Funding a Revolution examines the history of computing since World War II to elucidate the federal government's role in funding computing research, supporting the education of computer scientists and engineers, and equipping university research labs. It reviews the economic rationale for government support of research, characterizes federal support for computing research, and summarizes key historical advances in which government-sponsored research played an important role. Funding a Revolution contains a series of case studies in relational databases, the Internet, theoretical computer science, artificial intelligence, and virtual reality that demonstrate the complex interactions among government, universities, and industry that have driven the field. It offers a series of lessons that identify factors contributing to the success of the nation's computing enterprise and the government's role within it. |
| lisp machine learning: A Practical Introduction to Fuzzy Logic using LISP Luis Argüelles Mendez, 2015-09-11 This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book’s website. |
| lisp machine learning: Artificial Intelligence Masoud Yazdani, 1986 |
| lisp machine learning: Common LISP Guy Steele, 1990 The defacto standard - a must-have for all LISP programmers. In this greatly expanded edition of the defacto standard, you'll learn about the nearly 200 changes already made since original publication - and find out about gray areas likely to be revised later. Written by the Vice- Chairman of X3J13 (the ANSI committee responsible for the standardization of Common Lisp) and co-developer of the language itself, the new edition contains the entire text of the first edition plus six completely new chapters. They cover: - CLOS, the Common Lisp Object System, with new features to support function overloading and object-oriented programming, plus complete technical specifications * Loops, a powerful control structure for multiple variables * Conditions, a generalization of the error signaling mechanism * Series and generators * Plus other subjects not part of the ANSI standards but of interest to professional programmers. Throughout, you'll find fresh examples, additional clarifications, warnings, and tips - all presented with the author's customary vigor and wit. |
| lisp machine learning: Practical FP in Scala: a Hands-On Approach (2nd Edition) Gabriel Volpe, 2021-09-13 A book for intermediate to advanced Scala developers. Aimed at those who understand functional effects, referential transparency and the benefits of functional programming to some extent but who are missing some pieces to put all these concepts together to build a large application in a time-constrained manner.Throughout the chapters we will design, architect and develop a complete stateful application serving an API via HTTP, accessing a database and dealing with cached data, using the best practices and best functional libraries available in the Cats ecosystem such as Cats Effect, Fs2, Http4s, Skunk, Refined and others.You will also learn about common design patterns such as managing state, error handling and anti-patterns, all accompanied by clear examples. Furthermore, in the Bonus Chapter, we will dive into some advanced concepts such as MTL and Optics, and will explore Fs2 streams with a few interesting examples.A digital version is also available on LeanPub. |
| lisp machine learning: The Elements of Artificial Intelligence: Using Common LISP Steven L. Tanimoto, 1990 |
| lisp machine learning: Artificial Intelligence Manish Soni, 2024-11-13 Welcome to the world of Artificial Intelligence (AI)! This book is designed to provide you with a comprehensive introduction to the exciting field of Artificial Intelligence. Whether you are a student, a professional, or simply someone curious about the latest advancements in AI, this book aims to be your go-to resource. Artificial Intelligence has become an integral part of our daily lives, impacting industries such as healthcare, finance, transportation, and entertainment. As AI technologies continue to evolve, the demand for individuals with expertise in AI is on the rise. Whether you are pursuing a degree in computer science, aiming to enhance your career prospects, or simply fascinated by the endless possibilities of AI, this book is here to guide you on your journey. |
| lisp machine learning: Industrial And Engineering Applications Of Artificial Intelligence And Expert Systems Moonis Ali, M. Ali, 1988-08 |
| lisp machine learning: Artificial Intelligence Lavanya Sharma, Pradeep Kumar Garg, 2021-10-28 Artificial Intelligence: Technologies, Applications, and Challenges is an invaluable resource for readers to explore the utilization of Artificial Intelligence, applications, challenges, and its underlying technologies in different applications areas. Using a series of present and future applications, such as indoor-outdoor securities, graphic signal processing, robotic surgery, image processing, character recognition, augmented reality, object detection and tracking, intelligent traffic monitoring, emergency department medical imaging, and many more, this publication will support readers to get deeper knowledge and implementing the tools of Artificial Intelligence. The book offers comprehensive coverage of the most essential topics, including: Rise of the machines and communications to IoT (3G, 5G). Tools and Technologies of Artificial Intelligence Real-time applications of artificial intelligence using machine learning and deep learning. Challenging Issues and Novel Solutions for realistic applications Mining and tracking of motion based object data image processing and analysis into the unified framework to understand both IoT and Artificial Intelligence-based applications. This book will be an ideal resource for IT professionals, researchers, under or post-graduate students, practitioners, and technology developers who are interested in gaining insight to the Artificial Intelligence with deep learning, IoT and machine learning, critical applications domains, technologies, and solutions to handle relevant challenges. |
| lisp machine learning: Methods and Tools for Applied Artificial Intelligence Popovic, 1994-05-02 This work provides a comprehensive and coherent introduction to the expanding field of Artificial Intelligence (Al), explaining how knowledge-based systems are built, what tools and technologies are relevant and available, and how to employ them in specific situations. It pays special attention to the commercial intelligence systems that emerged in the '80s, as well as projecting the likely developments of the '90s. |
| lisp machine learning: Genetic Programming IV John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, Guido Lanza, 2005-09-14 Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law |
What is lisp used for today and where do you think it's going?
Actually Common Lisp is not only the extension language, but large parts of the application are written in Common Lisp (plus some C++). Other than that Lisp is a family of diverse dialects with …
What's so great about Lisp? - Stack Overflow
Jan 10, 2010 · Lisp is the Chuck Norris of programming languages. Lisp is the bar other languages are measured against. Knowing Lisp demonstrates developer enlightenment. I've heard of 3 …
syntax - What does # mean in LISP - Stack Overflow
Feb 2, 2011 · The reason is because Common Lisp tries to be economical with character usage in the language and leaves characters like [, ], {and } to the user for his/her own syntax extensions. …
lisp - What is an S-Expression - Stack Overflow
Oct 23, 2022 · Code in any language that amount to a value is an expression. Lisp code is just lists with elements, a fundmental datastructure in lisp, however the plan was to use a syntax (m …
scheme - What's the best way to learn LISP? - Stack Overflow
I'm a Common Lisp fan, but that may be one of those vi-vs-EMACS religious questions. For Scheme, go for Kent Dybvig's Scheme Programming Language, followed by SICP. For Common Lisp, as …
What's the difference between eq, eql, equal and equalp, in …
Feb 14, 2009 · From Common Lisp: Equality Predicates (eq x y) is true if and only if x and y are the same identical object. The eql predicate is true if its arguments are eq, or if they are numbers of …
Easy ways to try out and test Lisp syntax? - Stack Overflow
Jul 22, 2016 · If you just want to play with LISP, interactively, quickly, GNU Emacs has a LISP interpreter built in, and listening in the *scratch* buffer. Type an S-expression, position …
Lisp: list vs S-expression - Stack Overflow
May 27, 2012 · Today most Lisp program code is written using s-expressions. This is described here: McCarthy, Recursive Functions of Symbolic Expressions. In a Lisp programming language …
Why should I learn Lisp? [closed] - Stack Overflow
Jan 17, 2017 · Lisp is a large and complex language with a large and complex runtime to support it. For that reason, Lisp is best suited to large and complicated problems. Now, a complex problem …
What makes Lisp macros so special? - Stack Overflow
Nov 6, 2008 · But Lisp is different. Lisp macros do have access to the parser, and it is a really simple parser. A Lisp macro is not handed a string, but a preparsed piece of source code in the …
What is lisp used for today and where do you think it's going?
Actually Common Lisp is not only the extension language, but large parts of the application are written in Common Lisp (plus some C++). Other than …
What's so great about Lisp? - Stack Overflow
Jan 10, 2010 · Lisp is the Chuck Norris of programming languages. Lisp is the bar other languages are measured against. Knowing Lisp demonstrates …
syntax - What does # mean in LISP - Stack Overflow
Feb 2, 2011 · The reason is because Common Lisp tries to be economical with character usage in the language and leaves characters like [, ], {and } …
lisp - What is an S-Expression - Stack Overflow
Oct 23, 2022 · Code in any language that amount to a value is an expression. Lisp code is just lists with elements, a fundmental datastructure in lisp, …
scheme - What's the best way to learn LISP? - Stack Overflow
I'm a Common Lisp fan, but that may be one of those vi-vs-EMACS religious questions. For Scheme, go for Kent Dybvig's Scheme Programming …
