Your BMR is calories per day Your activity level is moderately active work out times per week Your activity factor is 1. Click here for the interactive Harris-Benedict Online calculator for men 4. The Katch McArdle Formula Use this formula for a very accurate estimate of your maintenance level if you know your body fat percentage and lean body mass. You weigh lbs.
It depends on what you want to do with the answer.
It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. This article walks you through how to use it.
Note To download the cheat sheet and follow along with this article, go to Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio.
This cheat sheet has a very specific audience in mind: That means that it makes some generalizations and oversimplifications, but it points you in a safe direction.
It also means that there are lots of algorithms not listed here. These recommendations are compiled feedback and tips from many data scientists and machine learning experts.
Sometimes none of them are a perfect fit. Several data scientists I talked with said that the only sure way to find the very best algorithm is to try all of them. Tip To download and print a diagram that gives an overview of the capabilities of Machine Learning Studio, see Overview diagram of Azure Machine Learning Studio capabilities.
Flavors of machine learning Supervised Supervised learning algorithms make predictions based on a set of examples. For instance, historical stock prices can be used to hazard guesses at future prices. Each example used for training is labeled with the value of interest—in this case the stock price.
A supervised learning algorithm looks for patterns in those value labels. Supervised learning is a popular and useful type of machine learning. With one exception, all the modules in Azure Machine Learning are supervised learning algorithms. There are several specific types of supervised learning that are represented within Azure Machine Learning: When the data are being used to predict a category, supervised learning is also called classification.
When there are more categories, as when predicting the winner of the NCAA March Madness tournament, this problem is known as multi-class classification. When a value is being predicted, as with stock prices, supervised learning is called regression.
Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect.
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.
Unsupervised In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping it into clusters or finding different ways of looking at complex data so that it appears simpler or more organized.
Reinforcement learning In reinforcement learning, the algorithm gets to choose an action in response to each data point.
The learning algorithm also receives a reward signal a short time later, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward.
Currently there are no reinforcement learning algorithm modules in Azure Machine Learning.Nov 01, · Research Resources. A Subject Tracer™ Information Blog developed and created by Internet expert, author, keynote speaker and consultant Marcus P.
Zillman, M.S. Java-Deserialization-Cheat-Sheet.
A cheat sheet for pentesters and researchers about deserialization vulnerabilities in various Java (JVM) serialization libraries. 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.
9. Summary: User research can be done at any point in the design cycle. This list of methods and activities can help you decide which to use when.
User-experience research methods are great at producing data and insights, while ongoing activities help get the right things done. Summary: Empathy maps, customer journey maps, experience maps, and service blueprints depict different processes and have different goals, yet they all build common ground within an organization.
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