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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 ...
In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates.. It is an efficient application of the chain rule to neural networks.
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 ...
A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [21] Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence of tokens.
Machine Learning. Ng, Andrew Y.; Jordan, Michael I. (2002). "On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes" (PDF). Advances in Neural Information Processing Systems. Jebara, Tony (2004). Machine Learning: Discriminative and Generative. The Springer International Series in Engineering and ...
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
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Pioneering machine learning research is conducted using simple algorithms. 1960s: 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