Название: The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond
Автор: Maria Han Veiga, François Gaston Ged
Издательство: De Gruyter
Год: 2024
Страниц: 210
Язык: английский
Формат: pdf (true), epub
Размер: 29.0 MB

This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. Machine Learning aims at building algorithms that autonomously learn how to perform a task from examples. This definition is rather vague on purpose, but to make it slightly clearer, by “autonomously” we mean that no expert is teaching (or coding by hand) the solution; by “learn” we mean that we have a measure of performance of the algorithm output on the task. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.

Название: Uncertainty Quantification with R: Bayesian Methods
Автор: Eduardo Souza de Cursi
Издательство: Springer
Год: 2024
Страниц: 493
Язык: английский
Формат: pdf (true)
Размер: 17.4 MB

This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of Bayesian uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes basic Bayesian probabilities, entropy, Bayesian estimation and decision, sequential Bayesian estimation, and numerical methods. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of Bayesian uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. This book targets the use of R, which is a GNU project to develop a tool for language and environment for statistical computing and graphics. An IDE is proposed by RStudio. The popularity of R and RStudio make that the reader will find on the web many sites and information about it. A wide literature can also be found about this software. The community of the users of R proposes a large choice of packages to extend the possibilities of R.

Название: Ultimate Machine Learning with Scikit-Learn: Unleash the Power of Scikit-Learn and Python to Build Cutting-Edge Predictive Modeling Applications and Unlock Deeper Insights Into Machine Learning
Автор: Parag Saxena
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 411
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn. Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes. Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines (SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis. By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.

Название: Лямбда-выражения в Java 8. Функциональное программирование – в массы
Автор: Уорбэртон Р.
Издательство: Москва
Год: 2014
Cтраниц: 192
Формат: pdf (ocr)
Размер: 21 мб
Язык: русский

Если вы имеете опыт работы с Java SE, то из этой книги узнаете об изменениях в версии Java 8, обусловленных появлением в языке лямбда-выражений. Вашему вниманию будут представлены примеры кода, упражнения и увлекательные объяснения того, как можно использовать эти анонимные функции, чтобы сделать код проще и чище, и как библиотеки помогают в решении прикладных задач.

Название: Cracking the Machine Learning Code: Technicality or Innovation?
Автор: KC Santosh, Rodrigue Rizk, Siddhi K. Bajracharya
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2024
Страниц: 143
Язык: английский
Формат: pdf (true), epub
Размер: 35.1 MB

Typically, applied AI use cases are limited to employing off-the-shelf Machine Learning models, and they range anywhere from healthcare and finance to autonomous systems and agriculture. The journey through technicalities and innovation in the Machine Learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of Artificial Intelligence (AI). The algorithms and techniques may evolve, but the essence of AI remains timeless. For any innovation by leveraging Machine Learning models on diverse applications/use cases, the focus (of innovation) typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost–efficiency and scalability. This book emphasizes the importance of moving beyond the constraints of pre-trained models and off-the-shelf basic building blocks to tackle real-world problems effectively. We navigate through three fundamental data types: numerical, textual, and image data, offering practical insights into their utilization across various domains. It is recommended that you become familiar with Python programming language as all the implementation is done with Python programming language. Firstly, we must start setting up an Interactive Development Environment (IDE) for coding Python and discuss some of the most important packages that are used in this book. Python is one of the most popular multi-purpose programming languages. The popularity comes from beginner-friendly syntax and a vast number of libraries and frameworks.

Название: Head First jаvascript Programming: A Learner’s Guide to Modern jаvascript, 2nd Edition (Second Early Release)
Автор: Eric Freeman, Elisabeth Robson
Издательство: O’Reilly Media, Inc.
Год: 2024-04-04
Страниц: 402
Язык: английский
Формат: epub
Размер: 55.8 MB

Now in its second edition, this brain-friendly guide is your comprehensive journey into modern jаvascript, covering everything from the core language fundamentals to cutting-edge features that define jаvascript today. You'll dive into the nuances of jаvascript types and the unparalleled flexibility of its functions. You'll learn how to expertly navigate classes and objects, and you'll finally understand closures. But that's just the beginning—you'll also get hands-on with the browser's document object model (DOM), engaging with jаvascript in ways you've only imagined. You won't just be reading—you'll be playing games, solving puzzles, pondering mysteries, and interacting with jаvascript. And you'll write real code, lots of it, so you can start building your own web applications.

Название: Programming C# 12: Build Cloud, Web, and Desktop Applications (3rd Early Release)
Автор: Ian Griffiths
Издательство: O’Reilly Media, Inc.
Год: 2024-03-11
Страниц: 1587
Язык: английский
Формат: pdf, epub
Размер: 11.1 MB

C# is undeniably one of the most versatile programming languages available to engineers today. With this comprehensive guide, you'll learn just how powerful the combination of C# and .NET can be. Author Ian Griffiths guides you through C# 12.0 and .NET 8 fundamentals and techniques for building cloud, web, and desktop applications. Designed for experienced programmers, this book provides many code examples to help you work with the nuts and bolts of C#, such as generics, LINQ, and asynchronous programming features. You'll get up to speed on .NET 8 and the latest C# 11.0 and 12.0 additions, including generic math, new polymorphism options, enhanced pattern matching, and new features designed to improve productivity. I have written this book for experienced developers—I’ve been programming for years, and I set out to make this the book I would want to read if that experience had been in other languages, and I were learning C# today. Whereas earlier editions explained some basic concepts such as classes, polymorphism, and collections, I am assuming that readers will already know what these are. The early chapters still describe how C# presents these common ideas, but the focus is on the details specific to C#, rather than the broad concepts.

Название: Think Python: How To Think Like a Computer Scientist, 3rd Edition (Third Early Release)
Автор: Allen B. Downey
Издательство: O’Reilly Media, Inc.
Год: 2024-04-10
Страниц: 651
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills. If you want to learn to program, you have come to the right place. Python is one of the best programming languages for beginners—and it is also one of the most in-demand skills. You have also come at the right time, because learning to program now is probably easier than ever. With virtual assistants like ChatGPT, you don’t have to learn alone. Throughout this book, I’ll suggest ways you can use these tools to accelerate your learning. This book is primarily for people who have never programmed before and people who have some experience in another programming language. If you have substantial experience in Python, you might find the first few chapters too slow.

Название: Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs at Scale (5th Early Release)
Автор: James Phoenix, Mike Taylor
Издательство: O’Reilly Media, Inc.
Год: 2024-03-13
Страниц: 440
Язык: английский
Формат: epub
Размер: 78.0 MB

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. The discipline of prompt engineering has arisen as a set of best practices for improving the reliability, efficiency, and accuracy of AI models.

Название: Hands-On Large Language Models: Language Understanding and Generation (6th Early Release)
Автор: Jay Alammar, Maarten Grootendorst
Издательство: O’Reilly Media, Inc.
Год: 2024-03-21
Страниц: 227
Язык: английский
Формат: epub
Размер: 11.0 MB

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. One of the most common tasks in natural language processing, and machine learning in general, is classification. The goal of the task is to train a model to assign a label or class to some input text. Categorizing text is used across the world for a wide range of applications, from sentiment analysis and intent detection to extracting entities and detecting language. We can use an LLM to represent the text to be fed into our classifier. The choice of this model, however, may not be as straightforward as you might think. Models differ in the language they can handle, their architecture, size, inference speed, architecture, accuracy for certain tasks, and many more differences exist. BERT is a great underlying architecture for representing tasks that can be fine-tuned for a number of tasks, including classification. Although there are generative models that we can use, like the well-known Generated Pretrained Transformers (GPT) such as ChatGPT, BERT models often excel at being fine-tuned for specific tasks.