## Learning Dexterity

Watch Video We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we’ve been working on for the past year. Dactyl…

## How to Generate Random Numbers in Python

The use of randomness is an important part of the configuration and evaluation of machine learning algorithms. From the random initialization of weights in an artificial neural network, to the splitting of data into random train and test sets, to the random shuffling of a…

## What is Statistics (and why is it important in machine learning)?

Statistics is a collection of tools that you can use to get answers to important questions about data. You can use descriptive statistical methods to transform raw observations into information that you can understand and share. You can use inferential statistical methods to reason from…

## OpenAI Five

Watch Video Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today we play with restrictions, we aim to beat a team of top professionals at The International in August subject only to a limited…

## A Gentle Introduction to Statistical Sampling and Resampling

Data is the currency of applied machine learning. Therefore, it is important that it is both collected and used effectively. Data sampling refers to statistical methods for selecting observations from the domain with the objective of estimating a population parameter. Whereas data resampling refers to…

## A Gentle Introduction to Normality Tests in Python

An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods. Parametric statistical methods assume that the data has a known and specific distribution, often a Gaussian distribution. If a data sample is not Gaussian, then…

## How to Use Correlation to Understand the Relationship Between Variables

There may be complex and unknown relationships between the variables in your dataset. It is important to discover and quantify the degree to which variables in your dataset are dependent upon each other. This knowledge can help you better prepare your data to meet the…

## Implementing Healthcare Interventions: Context is Key | PLOS Blogs Network

0000-0002-8715-2896 “One of the most important parts of our job is to listen to partners, adjust the strategies based on what they hear, and give implementers the leeway to use their expertise and their local VISIT THE SOURCE ARTICLE Implementing Healthcare Interventions: Context is Key…

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