Machine-Learning

Jupyter Notebooks transform coding into an interactive storytelling experience, making them indispensable for AI and data science workflows. This comprehensive guide takes you from absolute beginner to proficient user, with step-by-step instructions, AI-specific examples, and curated link resources to accelerate your journey.[1][2][3] Why Jupyter Notebooks Are Essential for AI Development Jupyter Notebooks combine executable code, visualizations, and narrative text in a single document, ideal for exploratory data analysis, model prototyping, and sharing AI experiments. Unlike traditional scripts, notebooks allow incremental execution, perfect for training machine learning models where you iterate on data preprocessing, feature engineering, and evaluation.[1][3] ...

Introduction “AI agents” are everywhere in current discourse: customer support agents, coding agents, research agents, planning agents. But the term is often used loosely, sometimes referring to: A single large language model (LLM) call A script that calls a model and then an API A complex system that plans, acts, remembers, and adapts over time To design, evaluate, or improve AI agents, you need a clear mental model of what an agent actually is and how its parts work together. ...

Large Language Models (LLMs) like GPT-2 and BERT have revolutionized natural language processing, and TensorFlow provides powerful tools to build, train, and deploy them. This detailed guide walks you through using TensorFlow and Keras for LLMs—from basics to advanced transformer architectures, fine-tuning pipelines, and on-device deployment.[1][2][4] Whether you’re prototyping a sentiment analyzer or fine-tuning GPT-2 for custom tasks, TensorFlow’s high-level Keras API simplifies complex workflows while offering low-level control for optimization.[1][2] ...

Large Language Models (LLMs) power everything from chatbots to code generation, but scaling them for training, fine-tuning, and inference demands distributed computing expertise. Ray, an open-source framework, simplifies this with libraries like Ray LLM, Ray Serve, Ray Train, and Ray Data, enabling efficient handling of massive workloads across GPU clusters.[1][5] This guide takes you from zero knowledge to hero status, covering installation, core concepts, hands-on examples, and production deployment. What is Ray and Why Use It for LLMs? Ray is a unified framework for scaling AI and Python workloads, eliminating the need for multiple tools across your ML pipeline.[5] For LLMs, Ray LLM builds on Ray to optimize training and serving through distributed execution, model parallelism, and high-performance inference.[1] ...

Large Language Models (LLMs) power tools like ChatGPT, revolutionizing how we interact with AI. This zero-to-hero guide takes you from foundational machine learning concepts to building, fine-tuning, and deploying LLMs, with curated link resources for hands-on learning.[1][2][3] Whether you’re a beginner with basic Python skills or an intermediate learner aiming for expertise, this post provides a structured path. We’ll cover theory, practical implementations, and pitfalls, drawing from top courses and tutorials. ...