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Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a British-American computer scientist and technology entrepreneur focusing on machine learning and artificial intelligence (AI). [2] Ng was a cofounder and head of Google Brain and was the former Chief Scientist at Baidu , building the company's Artificial Intelligence Group into a team of ...
TensorFlow is an open source software library powered by Google Brain that allows anyone to utilize machine learning by providing the tools to train one's own neural network. [2] The tool has been used to develop software using deep learning models that farmers use to reduce the amount of manual labor required to sort their yield, by training ...
During his undergraduate studies, he worked with Alex Smola on Kernel method in machine learning. [9] In 2007, Le moved to the United States to pursue graduate studies in computer science at Stanford University, where his PhD advisor was Andrew Ng.
When LDA machine learning is employed, both sets of probabilities are computed during the training phase, using Bayesian methods and an Expectation Maximization algorithm. LDA is a generalization of older approach of probabilistic latent semantic analysis (pLSA), The pLSA model is equivalent to LDA under a uniform Dirichlet prior distribution.
Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006. [3] The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007. [ 4 ] In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP). [ 2 ]
Download as PDF; Printable version; ... machine learning, deep reinforcement learning, ... He was the first PhD student of AI Professor Andrew Ng, who was a first ...
Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s: Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s: Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach.
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification , knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.