This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models and their inference.

Explore Python Physics Lesson 19 and learn how the Monte Carlo method can approximate Pi with simple yet powerful simulations. In this lesson, we break down the Monte Carlo technique step by step, ...

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...

Overview: Structured online platforms provide clear, step-by-step learning paths for beginners.Real progress in data science comes from hands-on projects and co ...

Learn momentum conservation by building a Python model of elastic collisions! This tutorial guides you step-by-step through simulating elastic collisions, analyzing momentum transfer, and visualizing ...

Abstract: Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Artificial intelligence is now used in healthcare, banking, education, media, and public services. The demand for AI knowledge is increasing every year. YouTube has become one of the easiest platforms ...