Our use of Artificial Intelligence is growing along with advancements in the field. It has gone to the point that it is used in riskier areas such as hiring, criminal justice, and healthcare. This is with the hope the AI will provide less biased results compared to humans. In their paper, Jake Silberg and James …

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Arguably one of the most important skills you must have in order to get started with using statistical methods is knowing the scale or level of measurement of your data. The appropriate method of analysis for your data is dependent on the scale  it was measured in. Here’s a quick rundown of the four levels …

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PyCon 2019 | Scikit-learn, wrapping your head around machine learning Speaker: Chalmer Lowe   A gentle introduction to machine learning through scikit-learn. This tutorial will enable attendees to understand the capabilities and limitations of machine learning through hands-on code examples and fun and interesting datasets. Learn when to turn to machine learning and which tools …

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PyCon 2019 | Pandas Is For Everyone Speaker: Daniel Chen   Data Science and Machine learning have been synonymous with languages like Python. Libraries like Numpy and Pandas have become the de facto standard when working with data. The DataFrame object provided by Pandas gives us the ability to work with heterogeneous unstructured data that …

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The normal distribution is arguably the most used distribution in statistics. A lot of statistical methods rely on assuming that your data is normally distributed.  What is so special about it? The infamous bell The normal distribution is characterized by its trademark bell-shaped curve. The shape of the bell curve is dictated by two parameters. …

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Google I/O 2019 | TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow TF-Agents is a clean, modular, and well-tested open-source library for Deep Reinforcement Learning with TensorFlow. This session will cover recent advancements in Deep RL, and show how TF-Agents can help to jump start your project. You will also see how TF-Agent library components …

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Google I/O 2019 | Machine Learning Fairness: Lessons Learned ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and …

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It can be easy to simply accept algorithms as indisputable mathematic truths. After all, who wants to spend their spare time deconstructing complex equations? But make no mistake: algorithms are limited tools for understanding the world, frequently as flawed and biased as the humans who create and interpret them. In this brief animation, which was adapted …

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Artificial neural networks were created to imitate processes in our brains, and in many respects – such as performing the quick, complex calculations necessary to win strategic games such as chess and Go – they’ve already surpassed us. But if you’ve ever clicked through a CAPTCHA test online to prove you’re human, you know that …

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Google I/O 2019 | Federated Learning: Machine Learning on Decentralized Data Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user’s device. Learn how this privacy-preserving technology is deployed in production in Google …

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