Vector Databases Explained: A Deep Dive into Pinecone Introduction In the age of AI, we are inundated with unstructured data—text from articles, pixels from images, soundwaves from audio clips. T...

Deep Learning Concepts for Interviews: A Guide for ML Engineers Introduction Deep Learning is no longer a niche subfield of AI; it is the engine driving the most significant breakthroughs in tech...

LLM Foundations Part 5: Fine-Tuning with LoRA and QLoRA Introduction Welcome to the fifth installment of our “LLM Foundations” series. In previous parts, we explored the architecture and pre-trai...

LLM Foundations Part 4: Building Real-World Applications Introduction Welcome to the final part of our LLM Foundations series. In Part 1, we understood the models. In Part 2, we learned how to pr...

LLM Foundations Part 3: The Essential Tools and Ecosystem Introduction In Part 1, we explored the architecture of LLMs, and in Part 2, we learned the art of prompting them. Now, we turn to the pr...

LLM Foundations Part 2: The Art of Prompt Engineering Introduction In Part 1 of our series, we explored the architecture and training of modern Large Language Models (LLMs). We learned that these...

LLM Foundations Part 1: A Guide to Modern Language Models Introduction Welcome to a new series exploring the foundations of the technology that has captured the world’s imagination: Large Languag...

ML Foundations: A Guide to Natural Language Processing (NLP) Introduction Welcome back to our machine learning foundations series. So far, we’ve dealt mostly with numerical data. But what about t...

ML Foundations: From Neural Networks to Transformer Models Introduction In our series so far, we’ve explored powerful machine learning algorithms that form the bedrock of data science. Now, we ve...

ML Foundations: A Deeper Dive into Decision Trees Introduction In a previous post, we introduced Decision Trees and Random Forests, highlighting their intuitive, flowchart-like structure. We lear...