// TAG: MACHINE LEARNING

Discover what Agentic Lightening is, why it's a game-changer for AI agent development, its key benefits, and a brief history. Learn how this Microsoft …

An introduction to TensorFlow 2.20.0, covering what it is, its benefits, use cases, a brief history, and step-by-step instructions for setting up your …

Explore the fundamental building blocks of semantic caching with Redis LangCache, including embeddings, vector similarity, and the lifecycle of cache …

Dive into the fundamental building blocks of Agentic Lightening. Understand the roles of `LitAgent`, `Trainer`, and the `AgentLightningServer`, and …

Learn how to seamlessly integrate your existing AI agents, built with frameworks like LangChain, OpenAI Agent SDK, AutoGen, and CrewAI, into Agentic …

Learn to build and train your first neural network using the high-level Keras API in TensorFlow 2.20.0, covering sequential and functional models, …

Delve into the critical concepts of 'rollouts' and 'rewards' in Agentic Lightening. Learn how agents execute tasks, how their interactions are …

Explore the advanced optimization algorithms supported by Agentic Lightening, including Reinforcement Learning (RL), Automatic Prompt Optimization …

Build a basic Question-Answering (QA) agent and leverage Agentic Lightening's capabilities for Automatic Prompt Optimization (APO) to enhance its …

Take your LangChain agent to the next level by integrating it with Agentic Lightening for Reinforcement Learning (RL) based optimization. This project …

A curated collection of resources to continue your journey with Agentic Lightening and the broader field of AI agents. Includes recommended courses, …

A comprehensive guide to Microsoft's Agentic Lightening framework, designed for absolute beginners and experienced developers to train and optimize AI …

A comprehensive guide to learning TensorFlow 2.20.0, from fundamental concepts to advanced applications, with practical examples and guided projects …

A comprehensive, beginner-friendly guide to Transformers.js, enabling state-of-the-art machine learning directly in web browsers. Learn from …

A complete guide to understanding data science and deep learning for fine-tuning, restructuring, quantizing, and retraining local LLMs like Ollama.

A comprehensive guide to understanding Large Language Model (LLM) architectures, from fundamental types and parameter counts to the specific …

A comprehensive guide to Large Language Model (LLM) quantization, covering its principles, various techniques (4-bit, 8-bit, GGUF), practical …

A comprehensive guide to deploying and serving Large Language Models (LLMs) locally, focusing on Ollama for running pre-packaged models, and …

A comprehensive guide to leveraging NumPy, Pandas, Matplotlib, and Seaborn for data science, from foundational concepts to advanced techniques, with a …

A comprehensive guide to Deep Learning with PyTorch, covering foundational tensor operations, neural network construction, custom training, and …

A comprehensive guide to Large Language Model (LLM) pre-training and fine-tuning concepts, covering Supervised Fine-tuning (SFT), Parameter-Efficient …

A comprehensive guide to traditional Machine Learning concepts and practical application with Scikit-learn, from basic regression and classification …

A comprehensive and practical guide to MLOps and LLMOps principles and practices for managing the lifecycle of Large Language Models and Agentic AI …

A comprehensive guide to Natural Language Processing fundamentals, including text preprocessing, word embeddings, and an in-depth exploration of …