Comprehensive Guide to AI Fundamentals and Algorithms
By
vinhnx
If you only eat one bagel today, this is the bagel.
Summary
The article provides a curated selection of articles on AI fundamentals, covering topics from building neural networks to training and evaluating them. It includes detailed sections on algorithms and architectures such as Linear and Logistic Regression, k-Nearest Neighbors, Clustering, Support Vector Machines, and advanced topics like Generative Adversarial Networks (GANs) and Diffusion Models. The content is part of Aman's AI Journal, which serves as course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.
Key quotes
· 3 pulledHere’s a hand-picked selection of articles on AI fundamentals/concepts that cover the entire process of building neural nets to training them to evaluating results.
Algorithms/Architecture: Linear and Logistic Regression, k-Nearest Neighbors, Clustering, Support Vector Machines (SVM), Naive Bayes, Decision Trees and Ensemble Methods.
Generative Adversarial Networks (GANs), Diffusion Models, Graph Neural Networks, Attention, Separable Convolutions.
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