Dive into Deep Learning

An interactive deep learning book with code, math, and discussions, based on the NumPy interface. https://d2l.ai/

URL: https://idao.world/

Higher School of Economics and Yandex are proud to announce the 3rd international data analysts olympiad.

The event is open to all teams and individuals, be they undergraduate, postgraduate or PhD students, company employees, researchers or new data scientists, .

The event aims to bridge the gap between the all-increasing complexity of Machine Learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions, but also to devise resource-efficient algorithms.

This will be a team machine learning competition, divided into two stages. The first stage will be online, open to all participants. The second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow.

LINK: https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/

Link: https://www.drivendata.org/competitions/59/camera-trap-serengeti/

Overview

Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!

In this competition, participants will predict the presence and species of wildlife in new camera trap data from the Snapshot Serengeti project, which boasts over 6 million images.

Camera traps are motion-triggered systems for passively collecting animal behavior data with minimal disturbance to their natural tendencies. Camera traps are an invaluable tool in conservation research, but the sheer amount of data they generate presents a huge barrier to using them effectively. This is where AI can help!

There are two immediate challenges where efforts like this competition are needed:

• Camera traps can’t automatically label the animals they observe, creating an immense (and sometimes prohibitive) burden on humans to determine where and what wildlife are present.
• Even when automated animal tagging models are available, the models that do exist don’t generalize well across time and locations, severely limiting their usefulness with new data.

To address these opportunities, we’re challenging data scientists, researchers, and developers from around the world to build the best algorithms for wildlife detection.

The competition is designed with a few objectives in mind:

• Innovation: Participants use state-of-the-art approaches in computer vision and AI and get live feedback on how well their solutions perform
• Generalization: This competition is designed to reward the best generalizable solutions. The private test data used to determine the winners will come entirely from the latest, unreleased season of data from the Snapshot Serengeti project (season 11). For more information on the competition timeline and evaluation, see the problem description.
• Execution: Models are trained locally and submitted to execute inference in the cloud – read on!
• Openness: All prize-winning models are released under an open source license for anyone to use and learn from

This is a brand new kind of DrivenData challenge! Previous models trained for camera trap images have often failed to generalize well. In this competition, we want to reward the models that generalize best to new images, so you won’t interact directly with the test set.  Rather than submitting your predicted labels for a test set you have, you’ll package up everything needed to do inference and send it to us. We’ll execute that code on Azure in a Docker container that has access to the test set images. By leveraging Microsoft Azure’s cloud computing platform and Docker containers, we’re moving our competition infrastructure one step closer to translating participants’ innovation into impact.

We can’t wait to run what you come up with!

National Data Science Challenge: https://careers.shopee.co.id/ndsc/

The objectives of the National Data Science Challenge (NDSC) are to:

• Bring the tech community closer through working together and knowledge sharing
• Provide an environment for the development of creative new ideas in Data Science
• Equip students and professionals with essential technical skills and expertise to prepare them for Industry 4.0

Through this competition, we hope to showcase the ubiquity and usefulness of data in getting insights, thus reinforcing Indonesia’s emphasis on driving the digital economy and the use of big data.

Lytft just released Level 5 Dataset, and they plan to release the corresponding challenge. Stay tuned.

Here is the dataset: https://level5.lyft.com/dataset/ . Its size is around 41 GB

Facebook has just introduced a great dataset and corresponding machine learning challenge at same time. The dataset is named LVIS (Large Vocabulary Instance Segmentation).

This is a great visual instance dataset.

The challenge: https://www.lvisdataset.org/challenge

Today, rigorous evaluation of general purpose object detectors is mostly performed in the few category regime (e.g. 80) or when there are a large number of training examples per category (e.g. 100 to 1000+). LVIS provides an opportunity to enable research in the setting where there are a large number of categories and where per-category data is sometimes scarce.
Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge.

The dataset: https://www.lvisdataset.org/dataset

Source: https://twitter.com/facebookai/status/1159548405867139074

Scikit-learn has just introduced simple API for machine learning visualizations. Here are a few visualization samples: