GraphGPTInfering graphs from textual data.
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Teaching - CS 230
My twin brother Afshine and I created this set of illustrated Deep Learning cheatsheets covering the content of the CS 230 class, which I TA-ed in Winter 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Deep Learning. • Types of layer, filter hyperparameters, activation functions • Object detection, face verification an
CS 230 - Convolutional Neural Networks Cheatsheet
By Afshine Amidi and Shervine Amidi Overview Architecture of a traditional CNN Convolutional neural networks, also known as CNNs, are a specific type of neural networks that are generally composed of the following layers: The convolution layer and the pooling layer can be fine-tuned with respect to hyperparameters that are described in the next sections. Types of layer Convolution layer (CONV) The
Shervine Amidi
Data Science track of the Computational and Mathematical Engineering department Research at the Stanford Vision Lab TA at Stanford's Computer Science and ICME departments Centrale Paris engineering curriculum (ECP 17) Research at the Center for Visual Computing (CVC) with Professors Evangelia I. Zacharaki and Nikos Paragios Teaching MIT With my twin brother Afshine, we built easy-to-digest study g
CS 229 - Deep Learning Cheatsheet
By Afshine Amidi and Shervine Amidi Neural Networks Neural networks are a class of models that are built with layers. Commonly used types of neural networks include convolutional and recurrent neural networks. Architecture The vocabulary around neural networks architectures is described in the figure below: By noting $i$ the $i^{th}$ layer of the network and $j$ the $j^{th}$ hidden unit of the lay
CS 229 - Supervised Learning Cheatsheet
By Afshine Amidi and Shervine Amidi Introduction to Supervised Learning Given a set of data points $\{x^{(1)}, ..., x^{(m)}\}$ associated to a set of outcomes $\{y^{(1)}, ..., y^{(m)}\}$, we want to build a classifier that learns how to predict $y$ from $x$. Type of prediction The different types of predictive models are summed up in the table below:
CS 229 - Unsupervised Learning Cheatsheet
By Afshine Amidi and Shervine Amidi Introduction to Unsupervised Learning Motivation The goal of unsupervised learning is to find hidden patterns in unlabeled data $\{x^{(1)},...,x^{(m)}\}$. Jensen's inequality Let $f$ be a convex function and $X$ a random variable. We have the following inequality:
CS 229 - Machine Learning Tips and Tricks Cheatsheet
By Afshine Amidi and Shervine Amidi Classification metrics In a context of a binary classification, here are the main metrics that are important to track in order to assess the performance of the model. Confusion matrix The confusion matrix is used to have a more complete picture when assessing the performance of a model. It is defined as follows:
Teaching - CS 229
My twin brother Afshine and I created this set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class, which I TA-ed in Fall 2018 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Machine Learning. Cheatsheet • Loss function, gradient descent, likelihood • Linear models, Support Vector Machines,
QuadriFlow
1Stanford University 2University of California, Berkeley 3Technical University of Munich Abstract QuadriFlow is a scalable algorithm for generating quadrilateral surface meshes based on the Instant Field-Aligned Meshes of Jakob et al. (ACM Trans. Graph. 34(6):189, 2015). We modify the original algorithm such that it efficiently produces meshes with many fewer singularities. Singularities in quadri
Efficient Online and Batch Learning using Forward-Backward Splitting
Efficient Online and Batch Learning using Forward-Backward Splitting Journal of Machine Learning Research, Volume 10, December 2009, pages 2873-2898 Long manuscript Early version including appendices published in Neural Information Processing Systems (NIPS 2009). Given as oral presentation. Slides. We describe, analyze, and experiment with a framework for empirical loss minimization with regulariz
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Index of /class/cs224n/slides
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Algorithms
http://stanford.edu/~lanhuong/refresher/notes/probstat-section3.pdf
Stephen P. Boyd – Books
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