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Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its Stanford AI Lab or SAIL). Ng has also worked in the field of online education, cofounding Coursera and DeepLearning.AI. [4] He has spearheaded many efforts to "democratize deep learning" teaching over 8 million students through his online courses.
In 2018, students of fast.ai participated in the Stanford’s DAWNBench challenge alongside big tech companies such as Google and Intel.While Google could obtain an edge in some challenges due to its highly specialized TPU chips, the CIFAR-10 challenge was won by the fast.ai students, programming the fastest and cheapest algorithms.
Finn investigates the capabilities of robots to develop intelligence through learning and interaction. [8] She has made use of deep learning algorithms to simultaneously learn visual perception and control robotic skills. [9] She developed meta-learning approaches to train neural networks to take in student code and output useful feedback. [10]
He authored and was the primary instructor of the first deep learning course at Stanford, CS 231n: Convolutional Neural Networks for Visual Recognition. [17] It became one of the largest classes at Stanford, growing from 150 students in 2015 to 750 in 2017.
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]