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Top 5 Data Science Sessions from GTC 2019

In this special edition of "This week in Data Science," we focus on the top 5 sessions for data scientists from GTC 2019, with links to the free sessions available on demand.

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Top 5 Data Science Sessions from GTC 2019

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  1. TOP DATA SCIENCE SESSIONS FROM GTC 2019

  2. THE PREMIER AI CONFERENCE NVIDIA’s GPU Technology Conference (GTC) is a global conference series providing training, insights, and direct access to experts on the hottest topics in computing today. 2

  3. LEADING INSIGHTS IN DATA SCIENCE AND MACHINE LEARNING SEE THE TOP 5 SESSIONS ON DATA SCIENCE FROM GTC. 3

  4. TOP 5 DATA SCIENCE SESSIONS #1 Gartner identifies AI as top megatrend RAPIDS CUDA DATAFRAME INTERNALS FOR C++ DEVELOPERS #1 #2 Deep learning is changing the world of sports HOW WALMART IMPROVES FORECAST ACCURACY WITH NVIDIA GPUS #2 #3 Opinion: “I was worried about AI until it saved my life” CONTEXT-AWARE NETWORK MAPPING AND ASSET CLASSIFICATION IN CYBER SECURITY #3 #4 PayPal uses AI to protect payments and performance #5 AI predicts melting of sea ice to save communities END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL DATASETS IN RAPIDS #4 #5 ACCELERATING GRAPH ALGORITHMS WITH RAPIDS 4

  5. RAPIDS CUDA DATAFRAME INTERNALS FOR C++ DEVELOPERS #1 The core of RAPIDS is CUDA DataFrame (cuDF), a library that provides Pandas-like DataFrame (a columnar data structure) functionality with GPU acceleration. cuDF provides a Python interface for use in existing data science workflows, and underneath cuDF is libcuDF, an open-source CUDA C++ library that provides a column data structure and algorithms to operate on these columns, such as filtering, selection, sorting, joining, and groupby. If you are interested in using GPU DataFrames via libcuDFs C/C++ interface, or if you are interested in contributing to the cuDF / libcuDF open source project, then this talk is for you. WATCH SESSION SOURCE: https://developer.nvidia.com/gtc/2019/video/S91043?ncid=so-sli-n2-84520 5

  6. HOW WALMART IMPROVES FORECAST ACCURACY WITH NVIDIA GPUS #2 In this talk we will show how GPU computing has enabled us to significantly improve forecast accuracy, and highlight the key bottlenecks that we have been able to overcome. We will provide runtime comparisons of CPU vs GPU-based algorithms on our real-world problems, and describe how GPU-based development works for us (hint: its easy to do.) We will also describe our collaboration with NVIDIA, who have been extremely helpful, continuously refining their algorithms and tools to better meet the needs of industry, and what tools and capabilities we see being especially useful for our path forward. WATCH SESSION SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9799-how+walmart+improves+forecast+accuracy+with+nvidia+gpus&ncid=so-sli-n2-84521 6

  7. CONTEXT-AWARE NETWORK MAPPING AND ASSET CLASSIFICATION IN CYBER SECURITY #3 Cybersecurity present unique challenges and need for fast iteration and quick exploration. We'll show how to leverage RAPIDS and GPU- accelerated data science to learn a network mapping from passively generated logs. We'll explain how near real-time ingest and processing capabilities allow us to visualize the network quickly and provide context to the security professional in a timely manner. WATCH SESSION SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9802-context-aware+network+mapping+and+asset+classification&ncid=so-sli-n2-84522 7

  8. END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL DATASETS IN RAPIDS #4 Location intelligence is key to understanding areas such as property insights, environmental monitoring, disaster management and prevention, traffic flows, and customer behavior. We'll describe how we used RAPIDS and cover our entire process, from processing raw data, merging sources, generating and labeling colorized voxel cubes for training, to model building, inference, and final application. WATCH SESSION SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9791-end-to-end+analysis+of+large+3d+geospatial+datasets+in+rapids&ncid=so-sli-n2-84523 8

  9. ACCELERATING GRAPH ALGORITHMS WITH RAPIDS #5 Graphs are a ubiquitous part of technology we use daily in systems like GPS graphs help find the shortest path between two points and in social networks, which use them to help users find friends. We'll explain why analyzing these vast networks with possibly billions of entries requires the computing power of GPUs. RAPIDS version 0.6 includes the first official release of cuGraph. WATCH SESSION SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9783-accelerating+graph+algorithms+with+rapids&ncid=so-sli-n2-84524 9

  10. STAY CURRENT ON THE LATEST INNOVATIONS IN DATA SCIENCE AND RAPIDS RAPIDS DATA SCIENCE

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