Accidents at sea happen all the time. Their costs – in terms of lives, money and environmental destruction – are huge. Wouldn’t it be great if they could be predicted and perhaps prevented? Dr. Yonit Hoffman’s Machine Learning Conference session discusses new ways of preventing sea accidents with the power of data science.
The deep learning company Dessa has open sourced Atlas, a deep learning platform. Though currently still in beta mode, it is designed to make running, evaluating and deploying deep learning projects easier. It works on macOS, Linux and Windows, and offers TensorBoard integration. Let’s take a closer look.
It’s not a secret that deep learning already made a revolution in several perception fields as vision, language and speech understanding and keeps pushing the frontiers. Take a tour of the final frontier for time series analysis in this advanced development session from the Machine Learning Conference.
PyTorch3D is the latest deep learning tool by Facebook AI. The open source tool is designed to integrate with PyTorch to make 3D deep learning easier. Along with it, the codebase of the 3D shape prediction method Mesh R-CNN, which was built with the help of PyTorch3D, has been released as well.
The AI research organization OpenAI has declared to standardize which deep learning framework to use in its projects, and the winner is PyTorch. Let’s take a closer look and see not only why OpenAI selected PyTorch, but also what benefits the standardization itself should offer.
ML Conference 2019 had lots of exciting talks and insights to offer. How can we make chatbots smarter and provide machines with abilities such as ethical values or emotional intelligence—and how can deep learning help save lives? We’ve collected 10 takeaways to share some highlights of our Berlin conference.
How can we detect deepfakes that have been created with deep learning methods such as GANs? Facebook, AWS and Microsoft joined forces to launch the Deepfake Detection Challenge (DFDC) that should encourage developers to research this issue. Winners can receive up to $500,000 USD.
In October, the machine learning library Apache SINGA graduated from the Apache Incubator. Apache SINGA was built with a focus on deep learning and its features make it suitable for a variety of use cases—from healthcare to industrial application. Its maintainers also have some further projects for deep learning in mind.
Honey bee colony assessment is usually carried out via the laborious manual task of counting and classifying comb cells. Beekeepers perform this task many times throughout the year to asses the colony’s strength and to track its development. As you can imagine, this is an extremely time-consuming and error-prone task.
Keras version 2.3.0 is here, and it is the last major multi-backend release. Going forward, users are recommended to switch their code over to tf.keras in TensorFlow 2.0. This release brings API changes and a few breaking changes. Have a look under the hood and see what it includes, as well as what the plans are going forward.
Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.
The latest open sourced tool from Facebook AI Research is Pythia, a deep learning framework designed to help with Visual Question Answering. It is built on top of the PyTorch framework and offers a modular design for building AI models. Take a peek at the research involved.
We’ve got another one to add to the impressive stack of deep learning uses. Spektral is for deep learning on graphs and uses the Keras API. While the project is still in progress, this Ph.D. worthy framework built in Python has everything you need for building graph neural networks.
Uber strikes back with the open sourcing another tool! This time, we take a look at Ludwig, is a toolbox that makes deep learning easier to understand for non-experts and faster for experts as well as researchers.
