Worried your ML models might blab about proprietary data? Now, developers can keep their training data isolated from their machine learning models with TensorFlow Privacy. This Python library optimizes ML models without running into any data security or privacy concerns with differential privacy.
Automating routine, boring tasks sounds great. Automation promises to rid developers of scutwork and let them focus on the meaty details. However, as Oren Eini explains, this is a snare that causes more problems than it solves.
Are you interested in the future of AI, humanity, and technology? A new digital magazine ConditioHumana.io explores the topics of humanity’s future in the increasingly digital world. We invite our readers to explore articles and interviews with voices from computer scientists, machine learning experts, and leaders in the humanities.
It’s here! Take a sneak peek at the upcoming sessions; there’s a lot for ML developers of all experience levels. Get ready to learn all about the latest innovations in machine learning at ML Conference 2019! Buy your tickets now and save big.
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.