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.
What is word2vec? This neural network algorithm has a number of interesting use cases, especially for search. In this excerpt from Deep Learning for Search, Tommaso Teofili explains how you can use word2vec to map datasets with neural networks.
Deep learning is always among the hottest topics and TensorFlow is one of the most popular frameworks out there. In this session, Khanderao Kand goes through some deep learning concepts in general and TensorFlow and Apache Spark in specific.
The field of artificial intelligence has shown tremendous progress in the past decade. But there’s more to AI than chess-playing robots. Mat Leonard, the Head of Udacity’s School of AI, explains how the history of deep learning is the history of a programming revolution. Are you ready for Software 2.0?
Just off a stint at Baidu’s AI Group, Andrew Ng is here to bring artificial intelligence to the masses. Deeplearning.ai and Coursera are teaming up to bring a new sequence of online classes to help you learn to code AI.
