From Ml Algorithms To Genai Llms
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From ML Algorithms to GenAI & LLMs
From ML Algorithms to GenAI & LLMs, Written by Aman Kharwal, founder of Statso.io, is the second edition of the book - Machine Learning Algorithms: Handbook. This book offers a comprehensive and expanded guide through the evolving world of machine learning and generative AI. Whether you are an experienced data scientist or just starting, this edition delivers practical insights and clear explanations of essential concepts like regression, classification, clustering, deep learning, and time series forecasting. This edition introduces two new chapters: "Mastering GenAI and LLMs" and "Understanding GANs for Generative AI with a Hands-on Project", which provide deep dives into large language models and generative adversarial networks (GANs). With hands-on Python code snippets and real-world project examples, the book bridges the gap between theory and application, offering you the tools to apply machine learning techniques effectively. Additional highlights include performance evaluation methods, data preprocessing techniques, feature engineering, and a quick reference appendix for tuning machine learning models. The book equips you with the necessary skills to navigate modern machine learning and AI, which makes it an essential resource for anyone interested in the field.
Generative AI and LLMs
Author: S. Balasubramaniam
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-09-23
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
Understanding Machine Learning
Author: Shai Shalev-Shwartz
language: en
Publisher: Cambridge University Press
Release Date: 2014-05-19
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.