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Explore the probabilistic foundations of linear regression in machine learning. Learn how assumptions about noise and priors lead to least squares and regularization techniques.
Explore the probabilistic foundations of linear regression in machine learning. Discover how assumptions about noise and priors lead to least squares and regularization techniques.
Explore how variational inference enables AI systems and brains to build efficient models from incomplete data. Learn about the evidence lower bound (ELBO) and its role in modern intelligent systems.
Explore the fundamental concepts of probability distributions and their crucial role in AI and machine learning. Learn how entropy, cross-entropy, and KL divergence shape modern algorithms.
Explore the fundamental algorithm behind machine learning systems like GPT and AlphaFold. Learn how backpropagation enables artificial neural networks to learn and why it differs from biological brains.
AI expert Andre Burkov shares his insights on large language models, their capabilities, limitations, and impact on the tech industry. He cuts through the hype to explain how these models really work.
Progress Software's study reveals alarming gaps in understanding and addressing AI data bias. Learn about the risks and strategies for responsible AI implementation.
Explore the current state of AI, its challenges, and future potential. Learn about expert models, regulatory concerns, and the path towards more advanced artificial intelligence.
Learn how to fine-tune AI models using MLX, including dataset creation, JSON-L formatting, and model quantization. This guide addresses common issues and provides practical tips for optimizing your fine-tuning process.