machine learning cheat sheet stanford pdf

I'll denote vectors with a little arrow on the top. Download PDF Version using the link below for the complete set of Theano Cheat Sheet . Ablative analysis Ablative analysis is analyzing the root cause of the difference in performance between the current and the baseline models. Eventually, I compiled over 20 Machine Learning-related cheat sheets. Warning. Cheat sheet 7: Stanford.edu. Do visit the Github repository, also, contribute cheat sheets if you have any. /Length 2133 Would you like to see this cheatsheet in your native language? VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning. Course Webpage for CS 217 Hardware Accelerators for Machine Learning, Stanford University. ML Cheatsheet Documentation 2 Basics. K-means Yes Values Predict categories or values? Email: cntaylor@stanford.edu 1. Regularization The regularization procedure aims at avoiding the model to overfit the data and thus deals with high variance issues. Deep Learning (Afshine Amidi) This is the third part of the cheat sheet series provided by the … We chose to work with python because of rich community Word embeddings. Activation function― Activation functions are use… ... stanford-cs-229-machine-learning / en / cheatsheet-unsupervised-learning.pdf Go to file Go to file T; Go to line L; Copy path afshinea Update cheatsheet. This Cheat Sheet is designed by Stanford University. stream @�:�1D��Žجgs⻲��z�}��\b��l��3��0�+��Q�9�)QDa~s5]&��^�7�I��.gQJ�OW�"ї�Jan��*� �x��;��Io�Q��a��kN83"��rSV�Ƙ��Kf��p��|K[�r�)�CnT1�f��!�c-s 6��N ��U��YR�ٗR�YGswd�0�'��m��c��(�Fl C If you find errors, please raise an issue or contribute a better definition! Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. Gordon Wetzstein! Download and print the Machine Learning Algorithm Cheat Sheet in tabloid size to keep it handy and get help choosing an algorithm. Machine Learning cheatsheets for Stanford's 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. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. Your decision is driven by both the nature of your data and the question you’re trying to answer. The following table sums up the different types of commonly used regularization techniques: Bias The bias of a model is the difference between the expected prediction and the correct model that we try to predict for given data points. Type of prediction― The different types of predictive models are summed up in the table below: Type of model― The different models are summed up in the table below: ... stanford-cs-229-machine-learning / en / cheatsheet-supervised-learning.pdf Go to file Go to file T; Go to line L; Copy path afshinea Update … Please note: I changed the notation very slighty. If you ﬁnd errors, please raise anissueorcontribute a better deﬁnition! Download the cheat sheet here: Machine Learning Algorithm Cheat Sheet (11x17 in.) /Filter /FlateDecode Confusion matrix The confusion matrix is used to have a more complete picture when assessing the performance of a model. Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. q+��lF�E��M\%0�9e�>��C��Ԍt�� sx�a+5�͹��,��V��4�۳p�"�ׁHF!�ɫHA+5��J�K��"J�?q��\k��yb{P}9?p�x�? ��~7"�;�~��̥�*��x�n�cs�6�i��Q=2_lo��r�n�o՟�i��Gϳ��'�.7+�7N�M�G�Bo]��B/vt��GXZ�qk��"��P��Eš�1 E͋t��]��k�y��/]��%�"L*[�L9. Quality, concise technical cheat sheets, on the other hand... not so much. This cheat sheet helps you choose the best Azure Machine Learning Studio algorithm for your predictive analytics solution. The Graphics Pipeline and OpenGL III: OpenGL Shading Language (GLSL 1.10)! Download PDF VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning. 190 0 obj Machine Learning Modelling in R : : CHEAT SHEET Standard Modelling Workflow Time Series View CC BY SA Arnaud Amsellem • thertrader@gmail.com • www.thertrader.com • Updated: 2018-03 Supervised & Unsupervised Learning Meta-Algorithm, Time Series & Model Validation Basics 1. >> Basics. A good set of resources covering theoretical machine learning concepts would be invaluable. It is defined as follows: Main metrics― The following metrics are commonly used to assess the performance of classification models: ROC― The receiver operating curve, also noted ROC, is the plot of TPR ve… It is meant ... Stanford Graduate School of Business. Stanford University!! Neural networks are a class of models that are built with layers. Available in العربية - English - Español - فارسی - Français - 한국어 - Português - Türkçe - Tiếng Việt - 简中 - 繁中. Machine Learning Cheat Sheet Cameron Taylor November 14, 2019 Introduction This cheat sheet introduces the basics of machine learning and how it relates to traditional econo-metrics. VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning. VIP Cheat Sheets - Machine Learning Tips by Stanford's CS 229 Students Download whole PDF of Supervised Learning Cheatsheet: From Here VIP Refresher: Probabilities and Statistics Cheatsheet Cons: None that I can see. Lecture 4! Latest commit 6dcf18d Oct 7, … LKuI&u#�A��d�Z��ZV�9v�u�(��v��3�I�x�(]��z|�-�F#�h�*�|e�Hәbm.��ģ(�l7o��y�s��-U�Su:!U#7��+��>��_�w\��u�P{�%"��a..��7��`��Vƛ��f�_k1��c��&��ܞ㠫�l�Dm'��j��n�f{}ب/��c/��G�ߟ��k1T+�!JW6��R�͞��+�T ��L}�2Y��e9uj�R=D�Q]��{��_�C��=�� &��8̵�^�;Ƨ�tQg��><=v�6+2�C2��ܳ/��&]�(b��[�"!x�xVrfz��!o2�R�r�b|�m-�&qu%�{��S��h��j�Ey]��q�}ƴ˸[Q��X�R� �o�5��YX)��-+1�R}�΃��y�_EWI�T�,;o.T�yHt'�ю1��`k��(9m�薷^�6=�6i]��WIU�PS 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. Hardware Accelerators for Machine Learning (CS 217) Stanford University, Winter 2020 ... Machine Learning Systems and Software Stack. Torch, Theano, Tensorflow) For programmatic models, choice of high-level language: Lua (Torch) vs. Python (Theano, Tensorflow) vs others. Confusion matrix― The confusion matrix is used to have a more complete picture when assessing the performance of a model. Students in my Stanford courses on machine learning have already made several useful suggestions, as have my colleague, Pat Langley, and my teaching Latest commit 6dcf18d Oct 7, … CS229–MachineLearning https://stanford.edu/~shervine Super VIP Cheatsheet: Machine Learning Afshine Amidiand Shervine Amidi August 12, 2018 Contents AI Open Education: Stanford Deep Learning Cheat Sheets in Japanese 30thOctober 2019, Code Chrysalis x MLT MiniConf#6 Yuta Kanzawa @yutakanzawa SFE Senior Analyst at Janssen Pharmaceutical K.K., Tokyo These metrics are are summed up in the table below: AUC The area under the receiving operating curve, also noted AUC or AUROC, is the area below the ROC as shown in the following figure: Basic metrics Given a regression model $f$, the following metrics are commonly used to assess the performance of the model: Coefficient of determination The coefficient of determination, often noted $R^2$ or $r^2$, provides a measure of how well the observed outcomes are replicated by the model and is defined as follows: Main metrics The following metrics are commonly used to assess the performance of regression models, by taking into account the number of variables $n$ that they take into consideration: where $L$ is the likelihood and $\widehat{\sigma}^2$ is an estimate of the variance associated with each response. View VIP Cheat Sheet (ML , DL , AI).pdf from CS 229 at Technical University of Valencia. Popular Deep Learning Frameworks Gluon: new MXNet interface to accelerate research Imperative: Imperative-style programs perform computation as you run them Symbolic: define the function first, then compile them The error is then averaged over the $k$ folds and is named cross-validation error. 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. Easily readable for beginners and advanced Tensorflow users alike. << Thanks. Scikit-learn algorithm. EE 267 Virtual Reality! Hardware Accelerators for Machine Learning (CS 217) Stanford University, Winter 2020 Networks Network Architectures Architectural Components/Motifs Regularization in Neural Networks Learning Ideas Datasets Contests Personalities Teams Tasks Events. ... stanford-cs-229-machine-learning / en / super-cheatsheet-machine-learning.pdf Go to file Go to file T; Go to line L; Copy path afshinea Update cheatsheet. The different types are summed up in the table below: The most commonly used method is called $k$-fold cross-validation and splits the training data into $k$ folds to validate the model on one fold while training the model on the $k-1$ other folds, all of this $k$ times. Source: All of these cheat sheets (and more) can be downloaded in pdf format from www.cheatsheets.aqeel-anwar.com. Pros: Easy to understand. CS229–MachineLearning https://stanford.edu/~shervine Super VIP Cheatsheet: Machine Learning Afshine Amidiand Shervine Amidi September 15, 2018 Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. 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 layer, we have: where we note $w$, $b$, $z$ the weight, bias and output respectively. Error analysis Error analysis is analyzing the root cause of the difference in performance between the current and the perfect models. It is accessible with an intermediate background in statistics and econometrics. VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning. x��Kw�F��,�9՘y1�]��ir�ԍ�l�.0˜"P��E{�0�`8qm)�X��}�pz��;��s��8��;! Warning: This document is under early stage development. the book is not a handbook of machine learning practice. :r�U��'`}��Er[5�� 3��-��@ȁn!��A��9��)X��H��Ժ$/�P�#ZX��#S��{�'��6�4�uK��\ �@�f��,e��c`��"��2�GX��3�mx� They can (hopefully!) Word2vec Word2vec is a framework aimed at learning word embeddings by estimating the likelihood that a given word is surrounded by other words. Goal. 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. You can help us, $\displaystyle\frac{\textrm{TP}+\textrm{TN}}{\textrm{TP}+\textrm{TN}+\textrm{FP}+\textrm{FN}}$, $\displaystyle\frac{\textrm{TP}}{\textrm{TP}+\textrm{FP}}$, How accurate the positive predictions are, $\displaystyle\frac{\textrm{TP}}{\textrm{TP}+\textrm{FN}}$, $\displaystyle\frac{\textrm{TN}}{\textrm{TN}+\textrm{FP}}$, $\displaystyle\frac{2\textrm{TP}}{2\textrm{TP}+\textrm{FP}+\textrm{FN}}$, Hybrid metric useful for unbalanced classes, $\displaystyle\frac{\textrm{FP}}{\textrm{TN}+\textrm{FP}}$, $\displaystyle\textrm{SS}_{\textrm{tot}}=\sum_{i=1}^m(y_i-\overline{y})^2$, $\displaystyle\textrm{SS}_{\textrm{reg}}=\sum_{i=1}^m(f(x_i)-\overline{y})^2$, $\displaystyle\textrm{SS}_{\textrm{res}}=\sum_{i=1}^m(y_i-f(x_i))^2$, $\displaystyle\frac{\textrm{SS}_{\textrm{res}}+2(n+1)\widehat{\sigma}^2}{m}$, $\displaystyle1-\frac{(1-R^2)(m-1)}{m-n-1}$, â¢ Training on $k-1$ folds and assessment on the remaining one, â¢ Training on $n-p$ observations and assessment on the $p$ remaining ones, Tradeoff between variable selection and small coefficients, $...+\lambda\Big[(1-\alpha)||\theta||_1+\alpha||\theta||_2^2\Big]$, â¢ Training error slightly lower than test error. Vocabulary When selecting a model, we distinguish 3 different parts of the data that we have as follows: Once the model has been chosen, it is trained on the entire dataset and tested on the unseen test set. Remark: learning the embedding matrix can be done using target/context likelihood models. be useful to all future students of this course as well as to anyone else interested in Machine Learning. Bias/variance tradeoff The simpler the model, the higher the bias, and the more complex the model, the higher the variance. This document is under early stage development. Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Caffe, DistBelief, CNTK) versus programmatic generation (e.g. �� $'A�|||n�'��/^Dp]��'e\$�$� ��g�fQ��L�S��i�8��}+��* ��Sn��C�G-��Fϗ��|�J�uIR��ȍS��D(G~Q��"�"��Ĺqq5��g[NI"�G-�E{U�J48D��x��S��_W��5;�X��)؍�w)ٍ?jE�!j�g馬da�9$�/��?��t �$��>��A uKd~}��]1s��SDmz��*t�_�y��|%��+��C�+ض�i�[�U��$P�t��>�9b�4�IЈ@�Ӟk�V��F�G75�R��J��p}�1HŬy��a4 R��DdB��y8�6��P]�Qd }�;��Q��ȧܹ\8��g�9�KH��WG�=�f��M�I��kY�-g^��Ȝ�s��G��u��:/H.6��7��F�ߟ�ty�� xn#(`��N��9��!��#0G��p�:'��s��`��@T? This cheat sheet shows you the ins and outs of Tensorflow what it is, how it works and how it compares to other data science tools compare. START No Yes Categories Predict future data points? Cheat sheets for machine learning are plentiful. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it. These are represented in the figure below: Cross-validation Cross-validation, also noted CV, is a method that is used to select a model that does not rely too much on the initial training set. Some I reference frequently and thought others may benefit from them too. Part 2: Machine Learning Cheat Sheets Machine Learning Cheat Sheets >>> If you like these cheat sheets, you can let me know here.<<< Machine Learning with Emojis Machine Learning with Emojis Cheat Sheet Machine Learning: Scikit Learn Cheat Sheet Scikit Learn Cheat Sheet. Deep-Learning Package Design Choices Model specification: Configuration file (e.g. Popular models include skip-gram, negative sampling and CBOW. Instead, my goal is to give the reader su cient preparation to make the extensive literature on machine learning accessible. Variance The variance of a model is the variability of the model prediction for given data points. This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. 2021 Cheatsheets: Data Science, Machine Learning, Deep Learning, Big Data and Artificial Intelligence Cheat Sheets Download Machine Learning Algorithms Cheat Sheet pdf | Data Science Cheat Sheet pdf | Tensorflow Cheat Sheet pdf | Github | Stanford | Scikit-Learn. :��5x��ЌVJ w!S#��g�T_��~ ��k��l�s�Q`�B�ಐe��YD�� I:�102��Q��"|�F@��8j\$1��2)�$n/�Od�*LruiO��k�>:��t�?mp�R��V��i�M�z��"E�"٨˃p��i^T׻Y �]\� 2�ȅu"gg��(��ͳǓ2$z��Gqj嫒 Commonly used types of neural networks include convolutional and recurrent neural networks. Example: $\vec\theta$ The octave tutorial that was part of the seond week is available as a script here.. Week 1 Introduction Machine Learning »Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance … Cheat Sheet 8: Tensorflow Core. It is defined as follows: Main metrics The following metrics are commonly used to assess the performance of classification models: ROC The receiver operating curve, also noted ROC, is the plot of TPR versus FPR by varying the threshold. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. ... stanford-cs-229-machine-learning / zh / cheatsheet-supervised-learning.pdf Go to file Go to file T; Go to line L; Copy path Cannot retrieve contributors at this time. Bias and Variance in Machine Learning … %�� Theano is the powerful deep learning library in python and this Cheat Sheet includes the most common ways to implement high-level neural networks API to develop and evaluate machine learning models. This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include: https:/stanford.edu/~shervine CS 229 – Machine Learning Super VIP Cheatsheet: Machine Learning 4 Machine %PDF-1.5 918 KB Download.