Neural networks are a class of models that are built with layers. Commonly used types of neural networks include convolutional and recurrent neural networks. Activation function Activation functions are used at the end of a hidden unit to introduce non-linear complexities to the model. Learning rate The learning rate, often noted $\alpha$ or sometimes $\eta$, indicates at which pace the weights get updated. This can be fixed or adaptively changed.

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. ROC The receiver operating curve, also noted ROC, is the plot of TPR versus FPR by varying the threshold. Once the model has been chosen, it is trained on the entire dataset and tested on the unseen test set. 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.

VIP Cheat Sheets - Deep Learning by Stanford's CS 229 Students Download whole PDF of Supervised Learning Cheatsheet: From Here 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 Download whole PDF of Probability and Statistics Cheatsheets: From Here VIP Refresher: Linear Algebra and Calculus Cheat Sheets Download whole PDF of Linear Algebra and Calculus Cheatsheets: From Here You may like this: 100 Free Machine Learning Books Super VIP Cheat Sheet: Machine Learning Download whole PDF of Super VIP Machine Learning Cheat Sheet: From Here

Remark: the vector $x$ defined above can be viewed as a $n\times1$ matrix and is more particularly called a column-vector. Remark: for matrices $A,B$, we have $(AB) T B TA T$. Remark: not all square matrices are invertible. Remark: $A$ is invertible if and only if $ A eq0$. Linearly dependence A set of vectors is said to be linearly dependent if one of the vectors in the set can be defined as a linear combination of the others.

Sample space The set of all possible outcomes of an experiment is known as the sample space of the experiment and is denoted by $S$. Event Any subset $E$ of the sample space is known as an event. That is, an event is a set consisting of possible outcomes of the experiment. If the outcome of the experiment is contained in $E$, then we say that $E$ has occurred. Axiom 1 ― Every probability is between 0 and 1 included, i.e: Axiom 2 ― The probability that at least one of the elementary events in the entire sample space will occur is 1, i.e: Axiom 3 ― For any sequence of mutually exclusive events $E_1, ..., E_n$, we have: Permutation A permutation is an arrangement of $r$ objects from a pool of $n$ objects, in a given order.

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. Latent variables Latent variables are hidden/unobserved variables that make estimation problems difficult, and are often denoted $z$. We note $c {(i)}$ the cluster of data point $i$ and $\mu_j$ the center of cluster $j$. Algorithm After randomly initializing the cluster centroids $\mu_1,\mu_2,...,\mu_k\in\mathbb{R} n$, the $k$-means algorithm repeats the following step until convergence: Algorithm It is a clustering algorithm with an agglomerative hierarchical approach that build nested clusters in a successive manner. In an unsupervised learning setting, it is often hard to assess the performance of a model since we don't have the ground truth labels as was the case in the supervised learning setting.

CS 229 ― Machine Learning 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.

Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand... not so much. A good set of resources covering theoretical machine learning concepts would be invaluable. Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. The VIP cheat sheets, as Shervine and Afshine have dubbed them (Github repo with PDFs available here), are structured around covering key top-level topics in Stanford's CS 229 Machine Learning course, including: You can visit Shervine's CS 229 resource page or the Github repo for more information, or can download the cheat sheets from the direct download links above.

Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand... not so much. A good set of resources covering theoretical machine learning concepts would be invaluable. Shervini Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. The VIP cheat sheets, as Shervini and Afshine have dubbed them (Github repo with PDFs available here), are structured around covering key top-level topics in Stanford's CS 229 Machine Learning course, including: You can visit Shervini's CS 229 resource page or the Github repo for more information, or can download the cheat sheets from the direct download links above.

Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Previous projects: A list of last year's final projects can be found here. Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: http://www.math.ucsd.edu/ For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, The official documentation is available here.