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| cs324 - large language models: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022-05-26 Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments |
| cs324 - large language models: Computer Vision Li Fei-Fei, 2013-02-01 When a 3-dimensional world is projected onto a 2-dimensional image, such as the human retina or a photograph, reconstructing back the layout and contents of the real-world becomes an ill-posed problem that is extremely difficult to solve. Humans possess the remarkable ability to navigate and understand the visual world by solving the inversion problem going from 2D to 3D. Computer Vision seeks to imitate such abilities of humans to recognize objects, navigate scenes, reconstruct layouts, and understand the geometric space and semantic meaning of the visual world. These abilities are critical in many applications including robotics, autonomous driving and exploration, photo organization, image, or video retrieval, and human-computer interaction. This book delivers a systematic overview of computer vision, comparable to that presented in an advanced graduate level class. The authors emphasize two key issues in modeling vision: space and meaning, and focus upon the main problems vision needs to solve, including: * mapping out the 3D structure of objects and scenes* recognizing objects* segmenting objects* recognizing meaning of scenes* understanding movements of humansMotivated by these important problems and centered on the understanding of space and meaning, the book explores the fundamental theories and important algorithms of computer vision, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene |
| cs324 - large language models: Graduate Catalog State University of New York at Binghamton, 1975 |
| cs324 - large language models: Decision Making Under Uncertainty Mykel J. Kochenderfer, 2015-07-24 An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines. |
| cs324 - large language models: Complexity in Chemistry, Biology, and Ecology Danail D. Bonchev, Dennis Rouvray, 2007-05-03 The book offers new concepts and ideas that broaden reader’s perception of modern science. Internationally established experts present the inspiring new science of complexity, which discovers new general laws covering wide range of science areas. The book offers a broader view on complexity based on the expertise of the related areas of chemistry, biochemistry, biology, ecology, and physics. Contains methodologies for assessing the complexity of systems that can be directly applied to proteomics and genomics, and network analysis in biology, medicine, and ecology. |
| cs324 - large language models: Synthetic Data for Deep Learning Sergey I. Nikolenko, 2021-06-26 This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy. |
| cs324 - large language models: Fundamentals of Artificial Intelligence K.R. Chowdhary, 2020-04-04 Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI. |
| cs324 - large language models: TinyML Pete Warden, Daniel Situnayake, 2019-12-16 Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size |
| cs324 - large language models: Speech & Language Processing Dan Jurafsky, 2000-09 |
| cs324 - large language models: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| cs324 - large language models: Neural Networks for Natural Language Processing S., Sumathi, M., Janani, 2019-11-29 Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing. |
| cs324 - large language models: Java Programming Ralph Bravaco, Shai Simonson, 2009-02-01 Java Programming, From The Ground Up, with its flexible organization, teaches Java in a way that is refreshing, fun, interesting and still has all the appropriate programming pieces for students to learn. The motivation behind this writing is to bring a logical, readable, entertaining approach to keep your students involved. Each chapter has a Bigger Picture section at the end of the chapter to provide a variety of interesting related topics in computer science. The writing style is conversational and not overly technical so it addresses programming concepts appropriately. Because of the flexibile organization of the text, it can be used for a one or two semester introductory Java programming class, as well as using Java as a second language. The text contains a large variety of carefully designed exercises that are more effective than the competition. |
| cs324 - large language models: Probabilistic Machine Learning Kevin P. Murphy, 2022-03-01 A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach. |
| cs324 - large language models: The Language of Food: A Linguist Reads the Menu Dan Jurafsky, 2014-09-15 A 2015 James Beard Award Finalist: Eye-opening, insightful, and huge fun to read. —Bee Wilson, author of Consider the Fork Why do we eat toast for breakfast, and then toast to good health at dinner? What does the turkey we eat on Thanksgiving have to do with the country on the eastern Mediterranean? Can you figure out how much your dinner will cost by counting the words on the menu? In The Language of Food, Stanford University professor and MacArthur Fellow Dan Jurafsky peels away the mysteries from the foods we think we know. Thirteen chapters evoke the joy and discovery of reading a menu dotted with the sharp-eyed annotations of a linguist. Jurafsky points out the subtle meanings hidden in filler words like rich and crispy, zeroes in on the metaphors and storytelling tropes we rely on in restaurant reviews, and charts a microuniverse of marketing language on the back of a bag of potato chips. The fascinating journey through The Language of Food uncovers a global atlas of culinary influences. With Jurafsky's insight, words like ketchup, macaron, and even salad become living fossils that contain the patterns of early global exploration that predate our modern fusion-filled world. From ancient recipes preserved in Sumerian song lyrics to colonial shipping routes that first connected East and West, Jurafsky paints a vibrant portrait of how our foods developed. A surprising history of culinary exchange—a sharing of ideas and culture as much as ingredients and flavors—lies just beneath the surface of our daily snacks, soups, and suppers. Engaging and informed, Jurafsky's unique study illuminates an extraordinary network of language, history, and food. The menu is yours to enjoy. |
| cs324 - large language models: Deep Learning Interviews Shlomo Kashani, 2020-12-25 The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs. |
| cs324 - large language models: Program Synthesis Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, 2017-07-11 Program synthesis is the task of automatically finding a program in the underlying programming language that satisfies the user intent expressed in the form of some specification. Since the inception of artificial intelligence in the 1950s, this problem has been considered the holy grail of Computer Science. Despite inherent challenges in the problem such as ambiguity of user intent and a typically enormous search space of programs, the field of program synthesis has developed many different techniques that enable program synthesis in different real-life application domains. It is now used successfully in software engineering, biological discovery, compute-raided education, end-user programming, and data cleaning. In the last decade, several applications of synthesis in the field of programming by examples have been deployed in mass-market industrial products. This monograph is a general overview of the state-of-the-art approaches to program synthesis, its applications, and subfields. It discusses the general principles common to all modern synthesis approaches such as syntactic bias, oracle-guided inductive search, and optimization techniques. We then present a literature review covering the four most common state-of-the-art techniques in program synthesis: enumerative search, constraint solving, stochastic search, and deduction-based programming by examples. It concludes with a brief list of future horizons for the field. |
| cs324 - large language models: Probabilistic Graphical Models Daphne Koller, Nir Friedman, 2009-07-31 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. |
| cs324 - large language models: Linguistic Structure Prediction Noah A. Smith, 2011 A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference |
| cs324 - large language models: Annual Catalogue of the University of Kansas University of Kansas, 1976 |
| cs324 - large language models: Robust Optimization Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski, 2009-08-10 Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject. |
| cs324 - large language models: Probability and Statistics Michael J. Evans, Jeffrey S. Rosenthal, 2004 Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students. |
| cs324 - large language models: #NoEstimates Vasco Duarte, 2015-09-15 How to always be on time, and not risk missing important deadlines or go over budget This book is the result of many years of hard work, and plenty of lessons learned. I wrote it because I believe we can do better than the accepted status quo in the software industry. It took me years to learn what I needed to learn to come up with my version of the #NoEstimates approach. You can do it in weeks! The techniques and ideas described here will help you explore the #NoEstimates universe in a very practical and hands-on manner. You will walk through Carmen's story. Carmen is a senior, very experienced project manager who is now confronted with a very difficult project. One would say, an impossible project. Through the book, and with the help of Herman, Carmen discovers and slowly adopts #NoEstimates which helps her turn that project around. Just like I expect it will help with the project you are in right now. The book also includes many concrete approaches you can use to adopt #NoEstimates, or just adopt those practices on their own. |
| cs324 - large language models: The Syntactic Process Mark Steedman, 2001-07-27 This book covers topics in formal linguistics, intonational phonology, computational linguistics, and experimental psycholinguistics, presenting them as an integrated theory of the language faculty. In this book Mark Steedman argues that the surface syntax of natural languages maps spoken and written forms directly to a compositional semantic representation that includes predicate-argument structure, quantification, and information structure without constructing any intervening structural representation. His purpose is to construct a principled theory of natural grammar that is directly compatible with both explanatory linguistic accounts of a number of problematic syntactic phenomena and a straightforward computational account of the way sentences are mapped onto representations of meaning. The radical nature of Steedman's proposal stems from his claim that much of the apparent complexity of syntax, prosody, and processing follows from the lexical specification of the grammar and from the involvement of a small number of universal rule-types for combining predicates and arguments. These syntactic operations are related to the combinators of Combinatory Logic, engendering a much freer definition of derivational constituency than is traditionally assumed. This property allows Combinatory Categorial Grammar to capture elegantly the structure and interpretation of coordination and intonation contour in English as well as some well-known interactions between word order, coordination, and relativization across a number of other languages. It also allows more direct compatibility with incremental semantic interpretation during parsing. The book covers topics in formal linguistics, intonational phonology, computational linguistics, and experimental psycholinguistics, presenting them as an integrated theory of the language faculty in a form accessible to readers from any of those fields. |
| cs324 - large language models: Programming Languages: Design and Implementation Terrence W. Pratt, 1975 |
| cs324 - large language models: Computation Structures Stephen A. Ward, Robert H. Halstead, 1990 Computer Systems Organization -- general. |
| cs324 - large language models: Programs for Special Populations , 1991 Describes six programmes for adults and children with mental and physical disabilities. It includes proven suggestions for recruiting and training a dedicated team of staff and volunteers, as well as insights into what it takes to start a recreational programme for people with disabilities. |
| cs324 - large language models: Software Evolution Tom Mens, Serge Demeyer, 2008-01-25 This book focuses on novel trends in software evolution research and its relations with other emerging disciplines. Mens and Demeyer, both authorities in the field of software evolution, do not restrict themselves to the evolution of source code but also address the evolution of other, equally important software artifacts. This book is the indispensable source for researchers and professionals looking for an introduction and comprehensive overview of the state-of-the-art. |
| cs324 - large language models: Cornell University Courses of Study Cornell University, 2007 |
| cs324 - large language models: Elements of Sequential Monte Carlo Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön, 2019-11-12 Written in a tutorial style, this monograph introduces the basics of Sequential Monte Carlo, discusses practical issues, and reviews theoretical results before guiding the reader through a series of advanced topics to give a complete overview of the topic and its application to machine learning problems. |
| cs324 - large language models: Probabilistic Databases Dan Suciu, Dan Olteanu, Christoph Koch, 2011 Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques |
| cs324 - large language models: An Introduction to Conditional Random Fields Charles Sutton, Andrew McCallum, 2012 An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields. |
| cs324 - large language models: Innovations in Technology Enhanced Learning Anton Ravindran, Liz Bacon, 2015 Innovations in Technology Enhanced Learning, edited by Dr Anton Ravindran and Professor Liz Bacon, is a collection of state-of-the-art research papers discussing innovations in the area of technology enhanced learning in adult education. It was inspired by ideas presented at the annual Computer Science Education: Innovation and Technology Conferences, organized and administered by Global Science and Technology Forum (GSTF). Input for the twelve chapters have been sourced from ten geographically dispersed countries from across the world: USA, Spain, Portugal, UK, Bahrain, Saudi Arabia, Malaysia, Singapore, Iran and Australia, providing a truly international perspective on the field. With rapid developments in the technology and delivery mechanisms including the development of MOOCs (Massive Open Online Courses), online learning is in the process of revolutionising higher education, which makes this book all the more relevant and timely. |
| cs324 - large language models: Low-Power Computer Vision George K. Thiruvathukal, Yung-Hsiang Lu, Jaeyoun Kim, Yiran Chen, Bo Chen, 2022-02-22 Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems. |
| cs324 - large language models: Graph Representation Learning William L. William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| cs324 - large language models: Bayes' Theorem Examples Dan Morris, 2016-10-02 ***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples ...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem... - Doug E. - TOP 100 REVIEWER...For those who are predominately Visual Learners, as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact. - Tin F. TOP 50 REVIEWER |
| cs324 - large language models: Guiding Young Children Patricia F. Hearron, Verna Hildebrand, 2005 The seventh edition of this popular book supports the authors' belief that guidance is more than getting children to do what you want them to do now; it is helping them to become everything they can become for all of their tomorrows. The book provides an overview, followed by discussion of core concepts, strategies for applying those concepts, and, finally, the broader perspective of professionalism and human resource development. Its approach focuses on the need to consider a child's developmental level as well as family and cultural context when planning environments and activities for young children. Unlike others in the field, it offers concrete suggestions on how to guide children while they are involved in specific activities such as playing, eating, napping, etc. For teachers and parents of young children. |
| cs324 - large language models: Database System Implementation Garcia-Molina, 2000-09 |
| cs324 - large language models: Computer Networking Olivier Bonaventure, 2016-06-10 Original textbook (c) October 31, 2011 by Olivier Bonaventure, is licensed under a Creative Commons Attribution (CC BY) license made possible by funding from The Saylor Foundation's Open Textbook Challenge in order to be incorporated into Saylor's collection of open courses available at: http: //www.saylor.org. Free PDF 282 pages at https: //www.textbookequity.org/bonaventure-computer-networking-principles-protocols-and-practice/ This open textbook aims to fill the gap between the open-source implementations and the open-source network specifications by providing a detailed but pedagogical description of the key principles that guide the operation of the Internet. 1 Preface 2 Introduction 3 The application Layer 4 The transport layer 5 The network layer 6 The datalink layer and the Local Area Networks 7 Glossary 8 Bibliography |
| cs324 - large language models: Advance Computing Technology R.. Buyya, |
| cs324 - large language models: Large Language Models Projects Pere Martra Manonelles, 2024-10-20 This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks |
Evaluating Large Language Models - stanford.5loi.com
In this assignment, you will evaluate large language models (LLMs). The assignment is decomposed into three components: each component progressively affords you more freedom …
Cs324 Large Language Models [PDF] - archive.ncarb.org
book covers probabilistic models introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables utility theory as a framework for …
CHAPTER 10 Large Language Models - Stanford University
We’ll work through an example of using large language mod- els to solve one classic NLP task of summarization (generating a short text that summarizes some larger document).
Large Language Models - GitHub Pages
We keep getting better performance as we scale the model, data, and compute up! Large Language Models demonstrate some human-like behaviors!
Build a Large Language Model (From Scratch)
An LLM, a large language model, is a neural network designed to understand, generate, and respond to human-like text. These models are deep neural networks trained on massive …
DATA 8005: Advanced Natural Language Processing - Tao Yu
Large language models (LMs) are able to learn from in-context examples about the task. However, there has been li ule understanding of how the model learns and which aspects of …
Project 2: Building Large Language Models - stanford.5loi.com
Project 2: Building Large Language Models CS324 (Winter 2022) Languagemodelsaretrainedonrawtextandthereforelackcertainproperties(e.g.,controllability,ability …
Extracting Training Data from Large Language Models
Contributions. In this work, we demonstrate that large lan-guage models memorize and leak individual training exam-ples. In particular, we propose a simple and efficient method for …
Large Language Models: the basics - Department of …
What defines a Large Language Model (LLM)? •Size? •Architecture? •Training objectives? •Anything can be called LLM if it’s good for the press release? •Intended Use (my preferred …
Lecture 10 Language models - slpcourse.github.io
Neural language model ‣ Calculating the probability of the next word in a sequence given some history using a neural network ‣ Neural network LMs far outperform n-gram language models
Cs324 Large Language Models (PDF) - archive.ncarb.org
book covers probabilistic models introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables utility theory as a framework for …
Introduction to Large Language Large Models Language Models
We can cast summarization as language modeling by giving a large language model a text, and follow the text by a token like tl;dr; this token is short for something like ‘too long; don’t read’ …
DATA 8005 Advanced Natural Language Processing
papers about large language models. Mostly presentations and discussions. I will deliver ⅓, while you will present the other ⅔. You are expected to come to the class regularly and participate in …
Large Language Model (LLM) - cs.arizona.edu
What is a language model? •Definition: A language model is a probability distribution over sequences of tokens. •token := a subword (word or part of word) •𝒱 := the set of tokens (aka …
Cs324 Large Language Models (Download Only)
under uncertainty from a computational perspective It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech …
Cs324 Large Language Models Full PDF - archive.ncarb.org
Cs324 Large Language Models: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall,Leandro von Werra,Thomas Wolf,2022-05-26 Since their introduction in …
Cs324 Large Language Models - archive.ncarb.org
Within the pages of "Cs324 Large Language Models," a mesmerizing literary creation penned with a celebrated wordsmith, readers set about an enlightening odyssey, unraveling the intricate …
Cs324 Large Language Models - archive.ncarb.org
Within the captivating pages of Cs324 Large Language Models a literary masterpiece penned with a renowned author, readers embark on a transformative journey, unlocking the secrets and …
Cs324 Large Language Models - archive.ncarb.org
extraordinary book, aptly titled "Cs324 Large Language Models," published by a highly acclaimed author, immerses readers in a captivating exploration of the significance of language and its …
Evaluating Large Language Models - stanford.5loi.com
In this assignment, you will evaluate large language models (LLMs). The assignment is decomposed into three components: each component progressively affords you more freedom …
Cs324 Large Language Models [PDF] - archive.ncarb.org
book covers probabilistic models introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables utility theory as a framework for …
Mina Lee Teaching Statement - Computer Science
• Large Language Models (or Foundation Models): This graduate-level course aims to explore the latest research on large language models, or foundation models [Bommasani et al., 2021]. …
CHAPTER 10 Large Language Models - Stanford University
We’ll work through an example of using large language mod- els to solve one classic NLP task of summarization (generating a short text that summarizes some larger document).
Large Language Models - GitHub Pages
We keep getting better performance as we scale the model, data, and compute up! Large Language Models demonstrate some human-like behaviors!
Build a Large Language Model (From Scratch)
An LLM, a large language model, is a neural network designed to understand, generate, and respond to human-like text. These models are deep neural networks trained on massive …
DATA 8005: Advanced Natural Language Processing - Tao Yu
Large language models (LMs) are able to learn from in-context examples about the task. However, there has been li ule understanding of how the model learns and which aspects of …
Project 2: Building Large Language Models - stanford.5loi.com
Project 2: Building Large Language Models CS324 (Winter 2022) Languagemodelsaretrainedonrawtextandthereforelackcertainproperties(e.g.,controllability,ability …
Extracting Training Data from Large Language Models
Contributions. In this work, we demonstrate that large lan-guage models memorize and leak individual training exam-ples. In particular, we propose a simple and efficient method for …
Large Language Models: the basics - Department of …
What defines a Large Language Model (LLM)? •Size? •Architecture? •Training objectives? •Anything can be called LLM if it’s good for the press release? •Intended Use (my preferred …
Lecture 10 Language models - slpcourse.github.io
Neural language model ‣ Calculating the probability of the next word in a sequence given some history using a neural network ‣ Neural network LMs far outperform n-gram language models
Cs324 Large Language Models (PDF) - archive.ncarb.org
book covers probabilistic models introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables utility theory as a framework for …
Introduction to Large Language Large Models Language …
We can cast summarization as language modeling by giving a large language model a text, and follow the text by a token like tl;dr; this token is short for something like ‘too long; don’t read’ …
DATA 8005 Advanced Natural Language Processing
papers about large language models. Mostly presentations and discussions. I will deliver ⅓, while you will present the other ⅔. You are expected to come to the class regularly and participate in …
Large Language Model (LLM) - cs.arizona.edu
What is a language model? •Definition: A language model is a probability distribution over sequences of tokens. •token := a subword (word or part of word) •𝒱 := the set of tokens (aka …
Cs324 Large Language Models (Download Only)
under uncertainty from a computational perspective It presents both the theory behind decision making models and algorithms and a collection of example applications that range from …
Cs324 Large Language Models Full PDF - archive.ncarb.org
Cs324 Large Language Models: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall,Leandro von Werra,Thomas Wolf,2022-05-26 Since their introduction in …
Cs324 Large Language Models - archive.ncarb.org
Within the pages of "Cs324 Large Language Models," a mesmerizing literary creation penned with a celebrated wordsmith, readers set about an enlightening odyssey, unraveling the intricate …
Cs324 Large Language Models - archive.ncarb.org
Within the captivating pages of Cs324 Large Language Models a literary masterpiece penned with a renowned author, readers embark on a transformative journey, unlocking the secrets and …
Cs324 Large Language Models - archive.ncarb.org
extraordinary book, aptly titled "Cs324 Large Language Models," published by a highly acclaimed author, immerses readers in a captivating exploration of the significance of language and its …
