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MIT 6.7960 Deep Learning, Fall 2024 Instructor: Sara Beery View the complete course: ...

What are the neurons, why are there layers, and what is the math underlying it? Help fund future projects: ...

Demystifying attention, the key mechanism inside transformers and LLMs. Instead of sponsored ad reads, these lessons are ...

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Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in ...

Learn about watsonx → https://ibm.biz/BdvxDm Get a unique perspective on what the difference is between Machine Learning ...

Cost functions and training for neural networks. Help fund future projects: https://www.patreon.com/3blue1brown Special thanks to ...

MIT 15.773 Hands-On Deep Learning Spring 2024 Instructor: Rama Ramakrishnan View the complete course: ...

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For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai ...

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big ...

Breaking down how Large Language Models work, visualizing how data flows through. Instead of sponsored ad reads, these ...

What's actually happening to a neural network as it learns? Help fund future projects: https://www.patreon.com/3blue1brown An ...

... computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary ...

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai This lecture provides a concise ...

Geometric Deep Learning: The Erlangen Programme of ML - ICLR 2021 Keynote by Michael Bronstein (Imperial College London ...

Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA ...

In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are ...