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🌟Just Finished Reading "The Innovator's Dilemma" by Clayton Christensen🌟 I finally tackled one of the classics in the business world, and wow, do…
🌟Just Finished Reading "The Innovator's Dilemma" by Clayton Christensen🌟 I finally tackled one of the classics in the business world, and wow, do…
Liked by Corey Nolet
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𝐖𝐢𝐧 𝐚 𝐆𝐞𝐅𝐨𝐫𝐜𝐞 𝐑𝐓𝐗 4080 𝐆𝐏𝐔 by watching a live chat between two AI industry leaders! For the first time publicly, NVIDIA CEO and…
𝐖𝐢𝐧 𝐚 𝐆𝐞𝐅𝐨𝐫𝐜𝐞 𝐑𝐓𝐗 4080 𝐆𝐏𝐔 by watching a live chat between two AI industry leaders! For the first time publicly, NVIDIA CEO and…
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Excited to share our latest blog post on NVIDIA's Developer Blog: "Building Cyber Language Models to Unlock New Cybersecurity Capabilities". In this…
Excited to share our latest blog post on NVIDIA's Developer Blog: "Building Cyber Language Models to Unlock New Cybersecurity Capabilities". In this…
Liked by Corey Nolet
Licenses & Certifications
Publications
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Bringing UMAP Closer to the Speed of Light with GPU Acceleration
Association for the Advancement of AI (AAAI) 2021
The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have resulted in inefficent and inaccurate versions of UMAP. We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up…
The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have resulted in inefficent and inaccurate versions of UMAP. We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up to 100x in practice. Many of these design choices/lessons are general purpose and may inform the conversion of other graph and manifold learning algorithms to use GPUs. Our implementation has been made publicly available as part of the open source RAPIDS cuML library (https://github.com/rapidsai/cuml)
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Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Multidisciplinary Digital Publishing Institute
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical…
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
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Courses
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Artificial Intelligence
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Big-Data Analytics with Hadoop
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Data Science
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Graph Analytics
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Logic & Reasoning
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Machine Learning
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Natural Language Processing
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Neural Networks
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More activity by Corey
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VideoPoet wins a best paper award at ICML! Check out the blog post for an overview (with lots of cool examples!) -- https://goo.gle/4atanoj
VideoPoet wins a best paper award at ICML! Check out the blog post for an overview (with lots of cool examples!) -- https://goo.gle/4atanoj
Liked by Corey Nolet
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Very few efforts to build RAG for enterprise scenarios are documented. This NVIDIA paper is a breath of fresh air! The NVIDIA team builds 3…
Very few efforts to build RAG for enterprise scenarios are documented. This NVIDIA paper is a breath of fresh air! The NVIDIA team builds 3…
Liked by Corey Nolet
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Fantastic to collaborate with Micron on BaM (https://lnkd.in/dKdqU2Aj), GIDS (https://lnkd.in/dQ4Ax8uW) and IGB (https://lnkd.in/gj-MsqyA).
Fantastic to collaborate with Micron on BaM (https://lnkd.in/dKdqU2Aj), GIDS (https://lnkd.in/dQ4Ax8uW) and IGB (https://lnkd.in/gj-MsqyA).
Liked by Corey Nolet
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For the best experience and TCO! Great read Jia Li Brandon Royal Renu Bhatia Mike Thomas Bailey Blake Eva Wasielewski Anne Brennan Hecht Charlie…
For the best experience and TCO! Great read Jia Li Brandon Royal Renu Bhatia Mike Thomas Bailey Blake Eva Wasielewski Anne Brennan Hecht Charlie…
Liked by Corey Nolet
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🚨 🧠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐄𝐮𝐜𝐥𝐢𝐝: 𝐀𝐧 𝐈𝐥𝐥𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐌𝐨𝐝𝐞𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡…
🚨 🧠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐄𝐮𝐜𝐥𝐢𝐝: 𝐀𝐧 𝐈𝐥𝐥𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐌𝐨𝐝𝐞𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡…
Liked by Corey Nolet
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