days
0
-18
-3
hours
-1
0
minutes
-1
-2
seconds
-4
-9
search

#deep learning

Watch Dr. Yonit Hoffman's Machine Learning Conference session

Data to the Rescue! Predicting and Preventing Accidents at Sea

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.

Atlas has a Python SDK, CLI & more on board

Deep learning platform Atlas is now open source

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.

What we learned at ML Conference 2019 in Berlin

Saving lives with deep learning, creating smarter chatbots and more: 10 takeaways from ML Conference 2019

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.

Distributed deep learning with Apache SINGA

Apache SINGA is now an Apache Top Level Project

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.

Watch Thiago da Silva Alves and Jean Metz's Machine Learning Conference 2019 session

Honey bee conservation using deep learning

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.

Machine learning is now seen as a silver bullet for solving all problems, but sometimes it is not the answer.

The Limitations of Machine Learning

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.

  • 1
  • 2