Machine Learning notebook

Welcome to this Machine Learning Notebook, designed to explore current key points and state-of-the-art techniques in the field. This notebook serves as a practical guide for understanding core concepts of machine learning and implementing advanced optimization techniques. We will cover topics such as mixed precision training, quantization, model optimization, and other essential strategies that are crucial for developing efficient, high-performance models.

Throughout the notebook, you’ll find explanations, code snippets, and examples that highlight how these techniques can improve model accuracy, speed up training times, and reduce computational costs, making it an invaluable resource for anyone looking to stay up-to-date with the latest advancements in machine learning.

• Core Machine Learning Concepts
• Machine Learning Methods
• Neural Networks and Deep Learning
• Model Optimization
• Mixed Precision Training and Quantization
• System Design for Machine Learning Models
• Large Language Models (LLMs)
• Machine Learning Methods in Image Processing
• Recommendation Systems: Overview and Methods
• Machine Learning for Time Series Data