Category: Blog

Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in machine learning.

Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of them) get updated.

Python has evolved from a simple scripting language to the backbone of modern data science and machine learning.

Machine learning workflows require several distinct steps — from loading and preparing data to creating and evaluating models.

The

A lot (if not nearly all) of the success and progress made by many generative AI models nowadays, especially large language models (LLMs), is due to the stunning capabilities of their underlying architecture: an advanced deep learning-based architectural model called the

This post is divided into three parts; they are: • Building a Semantic Search Engine • Document Clustering • Document Classification If you want to find a specific document within a collection, you might use a simple keyword search.

Using llama.

Machine learning models are trained on historical data and deployed in real-world environments.

Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.