This article explains why vector databases are useful in machine learning applications, how they work under the hood, and when you actually need one.
3 Ways to Speed Up Model Training Without More GPUs
In this article, we’ll look at three practical methods that consistently boost training performance without upgrading your hardware.
7 Feature Engineering Tricks for Text Data
From messy, raw text to clean, fully structured data features for AI and machine learning models: these simple tricks are all it takes.
10 Python One-Liners for Calling LLMs from Your Code
In this article, you will learn how to call popular large language models from Python using concise one-liners for both hosted APIs and local servers.
7 Pandas Tricks to Handle Large Datasets
7 tricks that are often overlooked but are simple and effective to implement when using Pandas library to manage large datasets more efficiently, from loading to processing and storing data optimally.
Building Transformer Models from Scratch with PyTorch (10-day Mini-Course)
You’ve likely used ChatGPT, Gemini, or Grok, which demonstrate how large language models can exhibit human-like intelligence. While creating a clone of these large language models at home is unrealistic and unnecessary, understanding how they work helps demystify their capabilities and recognize their limitations. All these modern large language models are decoder-only transformers. Surprisingly, their […]
The Machine Learning Practitioner’s Guide to Agentic AI Systems
Learn how to transition from traditional machine learning to agentic AI with practical frameworks, projects, and resources.
Build an Inference Cache to Save Costs in High-Traffic LLM Apps
In this article, you will learn how to add both exact-match and semantic inference caching to large language model applications to reduce latency and API costs at scale.
7 NumPy Tricks to Vectorize Your Code
This article walks through 7 vectorization techniques that eliminate loops from numerical code.
Is ChatGPT-5 Able to Provide Proofs for Advanced Mathematics?
In this article, you will learn how GPT-5 handles intermediate to advanced mathematical reasoning, including solving systems of equations and constructing clean, textbook-style proofs.