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His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years. [24] [25] As of 2020, three of most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone (#5), Neural Networks and Deep Learning (#6). [26]
Coursera Inc. (/ k ər ˈ s ɛ r ə /) is an American global massive open online course provider. It was founded in 2012 [2] [3] by Stanford University computer science professors Andrew Ng and Daphne Koller. [4] Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects.
A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are common examples of foundation models. [1]
One fall 2012 test by San Jose State and edX found that incorporating content from an online course into a for-credit campus-based course increased pass rates to 91% from as low as 55% without the online component. "We do not recommend selecting an online-only experience over a blended learning experience", says Coursera's Andrew Ng. [59]
Daphne Koller (Hebrew: דפנה קולר; born August 27, 1968) is an Israeli-American computer scientist. She was a professor in the department of computer science at Stanford University [4] and a MacArthur Foundation fellowship recipient. [1]
For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. [53] For Transformer-based LLM, training cost is much higher than inference cost.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
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