Top 5 Data Science Sessions from GTC 2019
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NVIDIA's GPU Technology Conference (GTC) 2019 featured prominent data science sessions focusing on advancements in AI and deep learning applications. The top five sessions included topics from GPU-based dataframes to enhancing forecast accuracy and cybersecurity solutions with RAPIDS, a library for GPU-accelerated data analysis. The conference highlighted the significance of using GPUs in improving computational efficiency across various real-world challenges.
Top 5 Data Science Sessions from GTC 2019
- 1. TOP DATA SCIENCE SESSIONS FROM GTC 2019
- 2. 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.
- 3. 3 LEADING INSIGHTS IN DATA SCIENCE AND MACHINE LEARNING SEE THE TOP 5 SESSIONS ON DATA SCIENCE FROM GTC.
- 4. 4 TOP 5 DATA SCIENCE SESSIONS Gartner identifies AI as top megatrend Deep learning is changing the world of sports Opinion: “I was worried about AI until it saved my life” PayPal uses AI to protect payments and performance AI predicts melting of sea ice to save communities #1 #2 #3 #4 #5 #1 RAPIDS CUDA DATAFRAME INTERNALS FOR C++ DEVELOPERS #2 HOW WALMART IMPROVES FORECAST ACCURACY WITH NVIDIA GPUS #3 CONTEXT-AWARE NETWORK MAPPING AND ASSET CLASSIFICATION IN CYBER SECURITY #4 END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL DATASETS IN RAPIDS #5 ACCELERATING GRAPH ALGORITHMS WITH RAPIDS
- 5. 5 RAPIDS CUDA DATAFRAME INTERNALS FOR C++ DEVELOPERS 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. SOURCE: https://developer.nvidia.com/gtc/2019/video/S91043?ncid=so-sli-n2-84520 #1 WATCH SESSION
- 6. 6 HOW WALMART IMPROVES FORECAST ACCURACY WITH NVIDIA GPUS 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. 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 #2 WATCH SESSION
- 7. 7 CONTEXT-AWARE NETWORK MAPPING AND ASSET CLASSIFICATION IN CYBER SECURITY 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. SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9802-context-aware+network+mapping+and+asset+classification&ncid=so-sli-n2-84522 #3 WATCH SESSION
- 8. 8 END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL DATASETS IN RAPIDS 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. 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 #4 WATCH SESSION
- 9. 9 ACCELERATING GRAPH ALGORITHMS WITH RAPIDS 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. SOURCE: https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9783-accelerating+graph+algorithms+with+rapids&ncid=so-sli-n2-84524 #5 WATCH SESSION
- 10. STAY CURRENT ON THE LATEST INNOVATIONS IN DATA SCIENCE AND RAPIDS RAPIDS DATA SCIENCE