Forwarded from Machine Learning with Python
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Forwarded from Machine Learning with Python
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Forwarded from Machine Learning with Python
Oxford University has launched a free course on "Deep Learning Theory" for the 2026-2027 academic year.
The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.
The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.
The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.
In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.
https://courses.maths.ox.ac.uk/course/view.php?id=6621
https://t.me/CodeProgrammer
The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.
The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.
The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.
In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.
https://courses.maths.ox.ac.uk/course/view.php?id=6621
https://t.me/CodeProgrammer
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Forwarded from Machine Learning
Columbia University has published a detailed educational resource on the Transformer architecture π .
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
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Forwarded from Machine Learning with Python
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