Posts in category

Machine Learning


The human race has long designed and used tools to help us solve problems, from flint axes to space shuttles. They affect our lives and shape society in expected and sometimes unexpected ways. We may understand how these tools work – after all, we built them – but sometimes it’s the use they’re put to …

Machine learning and artificial intelligence (AI) are some of the hottest buzzwords around, especially in the open source community. It seems that every month brings a new machine learning system, each focused on a different application. The good news is that since academics developed many of these frameworks, they are often open source by default. Even …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Deepfake videos are hard for untrained eyes to detect because they can be quite realistic. Whether used as personal weapons of revenge, to manipulate financial markets or to destabilize international relations, videos depicting people doing and saying things they never did or said are a fundamental threat to the longstanding idea that “seeing is believing.” …