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Explore how Boltzmann machines revolutionized AI by introducing stochasticity and hidden units, enabling creative data generation beyond simple pattern recall.
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.
OpenAI releases new models, Google launches Gemini 2.5 Flash, and AI video generation sees major advancements. This comprehensive roundup covers the latest developments in AI.
Explore how Hopfield networks model associative memory in the brain, allowing for rapid information retrieval and pattern completion. Learn about energy landscapes, neural dynamics, and the limitations of this groundbreaking approach.
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.
An in-depth look at the process of creating large language models, covering pre-training, post-training, data collection, evaluation, and system optimizations.