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Learn how to fine-tune the Mistral 7B model using MLX to convert natural language questions into SQL queries. This guide covers data preparation, model training, evaluation, and deployment to Hugging Face.
An in-depth exploration of back propagation in neural networks, covering forward and backward passes, loss functions, gradient descent, and weight updates.
Explore the two main approaches to representation learning: generative models and self-supervised learning. Understand their key differences and applications in machine learning.
An in-depth exploration of the key concepts behind generative and discriminative machine learning models, including probability theory, divergence minimization, and information theory.
An in-depth exploration of random variables, probability distributions, and their role in machine learning, covering key concepts like sample spaces, probability measures, and distribution functions.
An introduction to the fundamentals of probability theory, random variables, and their role in machine learning. Covers sample spaces, probability measures, and random variables as functions.
Discover effective strategies to address the issue of missing top-ranked documents in Retrieval-Augmented Generation (RAG) pipelines. Learn how to optimize your retrieval process for better results.
Explore the evolution of machine learning optimization techniques, from basic gradient descent to advanced algorithms like AdamW. Learn how these methods improve model performance and generalization.
Explore the evolution from basic RAG chatbots to sophisticated knowledge assistants. Learn about key components and techniques for building advanced AI systems.