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She teaches the Stanford course CS231n on "Deep Learning for Computer Vision," [79] whose 2015 version was previously online at Coursera. [80] She has also taught CS131, an introductory class on computer vision.
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]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
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]
Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was proposed in the 2017 paper "Attention Is All You Need". [1]
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 .
The Stanford Learning Lab completed its work in the spring of 2002 and was followed by The Stanford Center for Innovations in Learning (SCIL), which inherited core capabilities in technology development, educational program evaluation, and learning design and continued to perform research in these areas.