Analytics Competitions :

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Google Cloud & YouTube-8M Video Understanding Challenge

Can you produce the best video tag predictions?

Video captures a cross-section of our society. And major advances in analyzing and understanding video have the potential to touch all aspects of life from learning and communication to entertainment and play. In this competition, Google is inviting the Kaggle community to join efforts to accelerate research in large-scale video understanding, while giving participants early access to the Google Cloud Machine Learning (Cloud ML) beta platform.

Today, one of the greatest obstacles to rapid improvements in video understanding research has been the lack of large-scale, labeled datasets open to the public. For example, the availability of large, labeled datasets such as ImageNet has enabled continued breakthroughs in machine learning and machine perception. To that end, Google’s recent release of the YouTube-8M (YT-8M) dataset represents a significant step in this direction. Making this resource open to everyone from students and industry professionals is expected to kickstart innovation in areas such as representation learning and video modeling architectures.

In this competition, you are challenged to develop classification algorithms which accurately assign video-level labels using the new and improved YT-8M V2 dataset. The dataset was created from over 7 million YouTube videos (450,000 hours of video) and includes video labels from a vocabulary of 4716 classes (3.4 labels/video on average). It also comes with pre-extracted audio & visual features from every second of video (3.2B feature vectors in total). By taking part, Kagglers will not only play a pivotal role in setting state-of-the-art benchmarks, but also improve search and organization of video archives.

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3rd Annual Data Science Bowl Launch: Join the fight against cancer

Can you improve lung cancer detection?

In the United States, lung cancer strikes 225,000 people every year, and accounts for $12 billion in health care costs. Early detection is critical to give patients the best chance at recovery and survival.

Two years ago, the office of the U.S. Vice President spearheaded a bold new initiative, the Cancer Moonshot, to make a decade’s worth of progress in cancer prevention, diagnosis, and treatment in just 5 years.

In 2017, the Data Science Bowl will be a critical milestone in support of the Cancer Moonshot by convening the data science and medical communities to develop lung cancer detection algorithms.

Using a data set of thousands of high-resolution lung scans provided by the National Cancer Institute, participants will develop algorithms that accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that plagues the current detection technology, get patients earlier access to life-saving interventions, and give radiologists more time to spend with their patients.

This year, the Data Science Bowl will award $1 million in prizes to those who observe the right patterns, ask the right questions, and in turn, create unprecedented impact around cancer screening care and prevention. The funds for the prize purse will be provided by the Laura and John Arnold Foundation.

Visit DataScienceBowl.com to:
• Sign up to receive news about the competition
• Learn about the history of the Data Science Bowl and past competitions
• Read our latest insights on emerging analytics techniques

DSB 2017

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