Mathematics Of Machine Learning Subtitle A Comprehensive Primer On Linear Algebra Probability And Optimization Author Trevor Dash Book
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bookdown
bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.
Soft Computing and Machine Learning with Python
A definition states that the machine learning is a discipline that allows the computers to learn without explicit programming. The challenge in machine learning is how to accurately (algorithmic) describe some kinds of tasks that people can easily solve (for example face recognition, speech recognition etc.). Such algorithms can be defined for certain types of tasks, but they are very complex and/or require large knowledge base (e.g. machine translation MT). In many of the areas - data are continuously collected in order to get "some knowledge out of them" for example - in medicine (patient data and therapy), in marketing (the users / customers and what they buy, what are they interested in, how products are rated etc.). Data analysis of this scale requires approaches that will allow you to discover patterns and dependences among the data, that are neither known, nor obvious, but can be useful (data mining). Information retrieval - IR, is finding existing information as quickly as possible. For example, web browser - finds page within the (large) set of the entire WWW. Machine Learning - ML, is a set of techniques that generalize existing knowledge of the new information, as precisely as possible. An example is the speech recognition. Data mining - DM, primarily relates to the disclosure of something hidden within the data, some new dependence, which have not previously been known. Example is CRM - the customer analysis. Python is high-level programming language that is very suitable for web development, programming of games, and data manipulation / machine learning applications. It is object-oriented language and interpreter as well, allowing the source code to execute directly (without compiling). This edition covers machine learning theory and applications with Python, and includes chapters for soft computing theory, machine learning techniques/applications, Python language details, and machine learning examples with Python. Book jacket.
Applied Natural Language Processing in the Enterprise
Author: Ankur A. Patel
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-05-12
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production