Data Science Tutorials

Explore using Canopy as an open-source Retrieval Augmented Generation (RAG) framework and context built on top of the Pinecone vector database.

This article provides a comprehensive guide on utilizing the OpenCV library for image and video processing within a Python environment. We dive into the wide range of image processing functionalities OpenCV offers, from basic techniques to more advanced applications.

Improve your graphs in R with titles, text annotations, labelling of points, minor tick marks, reference lines, custom axes, and a chart legend.

Master exponents in Python using various methods, from built-in functions to powerful libraries like NumPy, and leverage them in real-world scenarios to gain a deeper understanding.

Dive into test-driven development (TDD) with our comprehensive Python tutorial. Learn how to write robust tests before coding with practical examples.

Learn how to create eye-catching Seaborn heatmaps

Learn how to automate model training, evaluation, versioning, and deployment using GitHub Actions with the easiest MLOps guide available online.

Discover how LangSmith optimizes LLM testing and debugging for AI applications. Enhance quality assurance and streamline development with real-world examples.

Explore 4 ways to detect NaN values in Python, using NumPy and Pandas. Learn key differences between NaN and None to clean and analyze data efficiently.

Most open-source LLMs have one huge drawback - short context length. Since context length is essentially the “memory” of LLMs, this issue needs to be addressed urgently. That’s exactly what Salesforce XGen 7B LLM does - provide an impressive 8k context length. This article is a tutorial on how to use and fine-tune it.

Learn all you need to know about the pandas .explode() method, covering single and multiple columns, handling nested data, and common pitfalls with practical Python code examples.

Fine-tuning Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), offering unprecedented capabilities in tasks like language translation, sentiment analysis, and text generation. This transformative approach leverages pre-trained models like GPT-2, enhancing their performance on specific domains through the fine-tuning process.