The FIFA World Cup 2022 may be over, but the story of Belgian striker Romelu Lukaku's performance against Croatia will be remembered as one of the tournament's most heartbreaking tales. Despite his high transfer fee tagged to his name, Lukaku's inability to convert easy chances led to Belgium's early exit. But what if there was a way to unlock the reasons behind those missed opportunities? With the power of AI in the generation of sports highlights, coaches, staff, and even Lukaku himself can uncover every move and decision made on the field, providing valuable insight and a chance for redemption in the next tournament. Additionally, to speed up the process of sports content generation, media providers are looking into ways to have AI analyze the game footage and automatically pick out the highlight-worthy moments.

In episode seven of the NVIDIA Grandmaster Series, you'll learn from four members of the Kaggle Grandmasters of NVIDIA (KGMON) team. Watch this video to learn how they used natural language processing to analyze argumentative writing elements from students and identified key phrases in patient notes from medical licensing exams. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a 4x Kaggle grandmaster. Dr. Christof Henkel, a Ph.D. in mathematics with a focus on probability theory and stochastic processes and is a senior deep learning scientist at NVIDIA.

Stanford University, CS230 is a widely revered course to learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Students learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. In the video lecture below, Andrew Ng Adjunct Professor, Computer Science, presents Lecture 8 which touches on career advice and also tips for reading research papers.

In this Data Science Salon talk, Kashif Rasul, Principal Research Scientist at Zalando, presents some modern probabilistic time series forecasting methods using deep learning. The Data Science Salon is a unique vertical focused conference which grew into the most diverse community of senior data science, machine learning and other technical specialists in the space.

Productionizing real-time ML models poses unique data engineering challenges for enterprises that are coming from batch-oriented analytics. Enterprise data, which has traditionally been centralized in data warehouses and optimized for BI use cases, must now be transformed into features that provide meaningful predictive signals to our ML models. Enterprises face the operational challenges of deploying these features in production: building the data pipelines, then processing and serving the features to support production models. ML data engineering is a complex and brittle process that can consume upwards of 80% of our data science efforts, all too often grinding ML innovation to a crawl. Based on experience building the Uber Michelangelo platform, and currently building next-generation ML infrastructure for Tecton.ai, the presentation shares insights on building a feature platform that empowers data scientists to accelerate the delivery of ML applications. Spark and DataBricks provide a powerful and massively scalable foundation for data engineering. Building on this foundation, a feature platform extends your data infrastructure to support ML-specific requirements. It enables ML teams to track and share features with a version-control repository, process and curate feature values to have a single source of centralized data, and instantly serve features for model training, batch, and real-time predictions.

With COVID-19 keeping everyone indoors, this is the perfect opportunity to brush up your data science skills. Data science is a field that is booming and is playing a huge role in society. Instead of just reading a book, in this regular feature column, I will provide some great video learning resources. You can follow these YouTubers and gain insights and advice from their years of experience in the field. Plus you can learn how to code by following through their tutorials and pick up a new skill.

Recent years have witnessed the boom of online sharing media contents, which raise significant challenges in effective management and retrieval. Though a large amount of efforts have been made, precise retrieval on video shots with certain topics has been largely ignored. At the same time, due to the popularity of novel time-sync comments, or so-called "bullet-screen comments", video semantics could be now combined with timestamps to support further research on temporal video labeling. In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. To be specific, due to the informal expression of bullet-screen comments, we first propose a temporal deep structured semantic model (T-DSSM) to represent comments into semantic vectors by taking advantage of their temporal correlation. Then, video highlights are recognized and labeled via semantic vectors in a supervised way. Extensive experiments on a real-world dataset prove that our framework could effectively label video highlights with a significant margin compared with baselines, which clearly validates the potential of our framework on video understanding, as well as bullet-screen comments interpretation.