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It became one of the largest classes at Stanford, growing from 150 students in 2015 to 750 in 2017. [ 18 ] Karpathy is a founding member of the artificial intelligence research group OpenAI , [ 19 ] [ 20 ] where he was a research scientist from 2015 to 2017. [ 18 ]
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]
Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-American computer scientist known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
su2code.github.io SU2 is a suite of open-source software tools written in C++ for the numerical solution of partial differential equations (PDE) and performing PDE-constrained optimization . The primary applications are computational fluid dynamics and aerodynamic shape optimization , [ 2 ] but has been extended to treat more general equations ...
The biologically inspired Hodgkin–Huxley model of a spiking neuron was proposed in 1952. This model describes how action potentials are initiated and propagated. . Communication between neurons, which requires the exchange of chemical neurotransmitters in the synaptic gap, is described in various models, such as the integrate-and-fire model, FitzHugh–Nagumo model (1961–1962), and ...
The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to their correct output. The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output.
PCFGs models extend context-free grammars the same way as hidden Markov models extend regular grammars.. The Inside-Outside algorithm is an analogue of the Forward-Backward algorithm.