2018 was a banner year for machine learning on GitHub. Projects like TensorFlow and PyTorch ranked among some of the most popular on the site, while Python carried on its dominance as a top programming language. It looks like the Octoverse is all about ML and we are 100% here for it.
Given the acceleration of change and increasing complexity of machine learning today, we can see many cases of high-profile samples of ML models not working as intended. In this article, SpringPeople Software shares some suggestions on how to make ML a better place.
Gradient-free optimization used to rely on custom implementation. But now, Facebook has just open sourced its Python3 toolkit for derivative-free optimization for improving machine learning parameters and hyperparameters. Optimize your models faster than ever with these tested algorithms!
Looking for a tool that can help you build the prettiest ML models this side of the Sistine Chapel? Take inspiration from Uber’s latest ML platform, Michelangelo PyML, which offers rapid deployment at scale for developers with a flexibility and power that would put David to shame.
Deep learning doesn’t have to stay science-fiction. When it comes to learning the latest ML, Keras is a great library for getting started and it can create some impressive feats. Where should you get started and why should you add it to your list of resolutions?
December is almost upon us so this is a good time to take a step back and look at some of the most trending technologies we saw in 2018, talk a bit about their status and what questions are raised about their prospects. Voting ends on Thursday!
Machine learning is complicated, but it’s becoming easier and easier to grasp with new tools and platforms. In preparation for AWS re:Invent 2018, AWS updated SageMaker. What are the newest features?
If you cannot or do not want to build an AI project from scratch, you have countless choices of ready-made services. But what can you do if the finished services do not fit the project? Customizable AI and ML models in the cloud, which you can train with your own data, provide a remedy.
Facebook is on a roll! The company recently announced that they would soon release some internal tools and they did not disappoint. The latest tool to be open sourced is Getafix, which learns from engineers’ past code fixes to recommend bug fixes. Getafix aims to let computers take care of the routine work under the watchful eye of a human. Let’s take a closer look.
Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between the two, and where should they turn when they need good predictions for their business KPIs.
Companies are looking for more ML talent. Prove you have the machine learning knowledge to get a data science job in one of the best fields in the US. In this article, Yana Yelina explores four of the most common methods for ML algorithms.
It’s been three months since Kubeflow 0.2 was released so now it’s time for 0.3 to shine. This release provides easier deployment and customization of components and better multi-framework support. In this article, we’ll have a look at some of the highlights.
New ways of handling large amounts of data by building more layers of artificial intelligence into computer systems have been allowing developers and businesses to create computer systems that work for them. In this article, Paul Bates explains why the future of consumerism and business optimization relies on machine learning and what role developers play in all this.
Have you moved on to reinforcement learning for ML? This new approach to machine learning is now supported by Horizon, an end-to-end platform that has just been open-sourced by Facebook. Let’s broaden our horizons and see what Horizon has to offer!