Data Science Tutorials

Learn the differences between RAG and Fine-Tuning techniques for customizing model performance and reducing hallucinations in LLMs.

Learn the fundamentals of data version control in DVC and how to use it for large datasets alongside Git to manage data science and machine learning projects.

This comprehensive, hands-on tutorial teaches you how to simplify deep learning model development with PyTorch Lightning. Perfect for beginners and experienced developers alike, it covers environment setup, model training, and practical examples.

Read our step-by-step beginner's guide to using Terraform, and learn how to efficiently automate and manage your Azure, AWS, and Google Cloud infrastructure.

Discover how Google BigQuery provides scalable, flexible, and cost-effective data warehousing and analytics solutions that seamlessly integrate with other GCP services.

Qwen is a family of large language and multimodal models developed by Alibaba Cloud, designed for various tasks like text generation, image understanding, and conversation.

Learn to use the Python pass statement in loops, functions, and classes. Understand the pass statement's importance in code execution and readability.

Unlock the full potential of Large Language Models (LLMs) with our guide on Chain-of-Thought (CoT) prompting. Learn how to enhance reasoning and problem-solving abilities in LLMs.

This article compares the performance of MatMul-free and traditional large language models in terms of speed and memory usage.

The yield keyword in Python turns a regular function into a generator, which produces a sequence of values on demand instead of computing them all at once.

This article presents a practical guide to implementing TNT-LLM, a framework that automates taxonomy generation and text classification using LLMs, demonstrated through a news article clustering example with GPT-4 and LangGraph.

Prompt chaining is a technique that involves breaking down a complex task into a series of smaller, interconnected prompts, where the output of one prompt serves as the input for the next, guiding the LLM through a structured reasoning process.