Lecture 1.2 - Multimodal Research Task (CMU Multimodal Machine Learning, Fall 2023)
Lecture 1.2 - Multimodal Research Task (CMU Multimodal Machine Learning, Fall 2023) Topics: research task, multimodal dataset, team project requirement ---------------------------------------------------------------------------------------------------------------- Carnegie Mellon University, 11-777 Multimodal Machine Learning, 2023 Fall Website: https://cmu-multicomp-lab.github.io/m... Instructor: Louis-Philippe Morency Co-lecturer: Paul Liang This revised version of CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification. This revised course is based on the new taxonomy introduced in this survey paper: https://arxiv.org/abs/2209.03430

Lecture 2.1 - Unimodal Representation - Part1 (CMU Multimodal Machine Learning, Fall 2023)

Lecture 3.2 - Multimodal Coordination and Fission (CMU Multimodal Machine Learning, Fall 2023)

AI Study Group Dr David Hoyle- The Mathematics behind LLMs and Transformers

Professor Ruslan Salakhutdinov, CMU, exVP of Research at Meta, Ex-Director of AI Research at Apple

Exclusive Interview with Nobel Prize Winner John Jumper: AI's Next Frontier After AlphaFold

AI Without Human Control : Risks and Challenges | AgenTrix 2026

Inside the Mind of Anthropic CEO Dario Amodei | The Circuit | Extended Interview

Multimodality and Data Fusion Techniques in Deep Learning

Lecture 3.1 - Multimodal Representation Fusion (CMU Multimodal Machine Learning, Fall 2023)

BERTopic for Topic Modeling - Maarten Grootendorst - Talking Language AI Ep#1
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
Yann LeCun's $1B Bet Against LLMs [Part 1]

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

PMAP 8921 • (1) Truth, beauty, and data: Example 3: Getting started with RStudio and projects

You Can't Always Get What You (Prompt): From Specification to Observation in Generative Interfaces

Lecture 7.2 - Multimodal Inference and Knowledge (CMU Multimodal Machine Learning, Fall 2023)

This is not the AI we were promised | The Royal Society

Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)

Lecture 4.1 - Multimodal Alignment (CMU Multimodal Machine Learning, Fall 2023)

How To Think SO CLEARLY People Assume You're A Genius

