# AI News, Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning BigData

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## Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning BigData

Code Snippets and Github Included chatbotslife.com Machine Learning Overview Machine Learning Cheat Sheet Machine Learning: Scikit-learn algorithm This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part.

- On Thursday, December 14, 2017
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## Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning BigData

Code Snippets and Github Included chatbotslife.com Machine Learning Overview Machine Learning Cheat Sheet Machine Learning: Scikit-learn algorithm This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part.

- On Thursday, December 14, 2017
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## Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio

mar. 2017 5 minutes to read Contributors The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for a predictive analytics model.

Download: Machine learning algorithm cheat sheet Download the cheat sheet here: Machine Learning Algorithm Cheat Sheet (11x17 in.) Download and print the Machine Learning Algorithm Cheat Sheet in tabloid size to keep it handy and get help choosing an algorithm.

More help with algorithms For help in using this cheat sheet for choosing the right algorithm, plus a deeper discussion of the different types of machine learning algorithms and how they're used, see How to choose algorithms for Microsoft Azure Machine Learning.

Notes and terminology definitions for the machine learning algorithm cheat sheet The suggestions offered in this algorithm cheat sheet are approximate rules-of-thumb.

It is a common approach in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot’s next action.

For example, if the data being recorded is the number of minutes until the next subway train arrives, two measurements taken a day apart are statistically independent.

For example, in daily high/low temperature data, knowing the low temperature for the day allows you to make a reasonable guess at the high.

In the ensemble approach, there is a separate two-class classifier for each class - each one separates the data into two categories: “this class” and “not this class.” Then these classifiers vote on the correct assignment of the data point.

Often this is a characteristic of the data that you don’t know until after you’ve tried to separate it, but it’s something that typically can be learned by visualizing beforehand.

If the class boundaries look very irregular, stick with decision trees, decision jungles, support vector machines, or neural networks.

Neural networks can be used with categorical variables by creating a dummy variable for each category, setting it to 1 in cases where the category applies, 0 where it doesn’t.

- On Thursday, December 14, 2017
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## How to choose algorithms for Microsoft Azure Machine Learning

This cheat sheet has a very specific audience in mind: a beginning data scientist with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio.

Most of the statements of disagreement begin with 'It depends…' How to use the cheat sheet Read the path and algorithm labels on the chart as 'For <path label="">, use <algorithm>.'

It can use any information that might be relevant—the day of the week, the season, the company's financial data, the type of industry, the presence of disruptive geopolitical events—and each algorithm looks for different types of patterns.

After the algorithm has found the best pattern it can, it uses that pattern to make predictions for unlabeled testing data—tomorrow's prices.

The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.

Reinforcement learning is common in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot's next action.

Non-linear class boundary - relying on a linear classification algorithm would result in low accuracy Data with a nonlinear trend - using a linear regression method would generate much larger errors than necessary Despite their dangers, linear algorithms are very popular as a first line of attack.

They are numbers that affect the algorithm's behavior, such as error tolerance or number of iterations, or options between variants of how the algorithm behaves.

While this is a great way to make sure you've spanned the parameter space, the time required to train a model increases exponentially with the number of parameters.

Algorithm Accuracy Training time Linearity Parameters Notes Two-class classification logistic regression ● ● 5 decision forest ● ○ 6 decision jungle ● ○ 6 Low memory footprint boosted decision tree ● ○ 6 Large memory footprint neural network ● 9 Additional customization is possible averaged perceptron ○ ○ ● 4 support vector machine ○ ● 5 Good for large feature sets locally deep support vector machine ○ 8 Good for large feature sets Bayes’ point machine ○ ● 3 Multi-class classification logistic regression ● ● 5 decision forest ● ○ 6 decision jungle ● ○ 6 Low memory footprint neural network ● 9 Additional customization is possible one-v-all - - - - See properties of the two-class method selected Regression linear ● ● 4 Bayesian linear ○ ● 2 decision forest ● ○ 6 boosted decision tree ● ○ 5 Large memory footprint fast forest quantile ● ○ 9 Distributions rather than point predictions neural network ● 9 Additional customization is possible Poisson ● 5 Technically log-linear.

For predicting counts ordinal 0 For predicting rank-ordering Anomaly detection support vector machine ○ ○ 2 Especially good for large feature sets PCA-based anomaly detection ○ ● 3 K-means ○ ● 4 A clustering algorithm Algorithm properties: ●

- shows good accuracy and moderate training times Algorithm notes Linear regression As mentioned previously, linear regression fits a line (or plane, or hyperplane) to the data set.

Data with a linear trend Logistic regression Although it confusingly includes 'regression' in the name, logistic regression is actually a powerful tool for two-class and multiclass classification.

The fact that it uses an 'S'-shaped curve instead of a straight line makes it a natural fit for dividing data into groups.

logistic regression to two-class data with just one feature - the class boundary is the point at which the logistic curve is just as close to both classes Trees, forests, and jungles Decision forests (regression, two-class, and multiclass), decision jungles (two-class and multiclass), and boosted decision trees (regression and two-class) are all based on decision trees, a foundational machine learning concept.

decision tree subdivides a feature space into regions of roughly uniform values Because a feature space can be subdivided into arbitrarily small regions, it's easy to imagine dividing it finely enough to have one data point per region.

In order to avoid this, a large set of trees are constructed with special mathematical care taken that the trees are not correlated.

Fast forest quantile regression is a variation of decision trees for the special case where you want to know not only the typical (median) value of the data within a region, but also its distribution in the form of quantiles.

This combination of simple calculations results in the ability to learn sophisticated class boundaries and data trends, seemingly by magic.

The boundaries learned by neural networks can be complex and irregular The two-class averaged perceptron is neural networks' answer to skyrocketing training times.

typical support vector machine class boundary maximizes the margin separating two classes Another product of Microsoft Research, the two-class locally deep SVM is a non-linear variant of SVM that retains most of the speed and memory efficiency of the linear version.

Within the Azure Machine Learning collection, there are algorithms that specialize in: rank prediction (ordinal regression), count prediction (Poisson regression), anomaly detection (one based on principal components analysis and one based on support vector machines) clustering (K-means) PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution;

data set is grouped into five clusters using K-means There is also an ensemble one-v-all multiclass classifier, which breaks the N-class classification problem into N-1 two-class classification problems.

pair of two-class classifiers combine to form a three-class classifier Azure Machine Learning also includes access to a powerful machine learning framework under the title of Vowpal Wabbit. </algorithm></path>

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