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DataScience

Essentials

Achieve AI Performance from Datacenter to Edge

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ON DEMAND • 58:18 mins
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Essentials

Achieve High-Performance Scaling for E2E Machine Learning and Data Analytics Workflows

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ON DEMAND • 47:40 mins
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Essentials

Solve Enhanced Math Problems on GPUs: Linear Algebra, Sparse Matrices, and RNGs

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ON DEMAND • 48:30 mins
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Article

Bringing Accelerated Analytics at Scale to Intel® Architecture

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Learn how OmniSci—a big data analytics and visualization company—and Intel are advancing and unifying the worlds of data science and analytics on today's CPUs and GPUs. (Includes download links.)
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Essentials

Intel® Optimization for TensorFlow*: Tips & Tricks for AI+HPC Convergence

4.5/5 (2)
ON DEMAND • 24:03 mins
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Essentials

Fast, Scalable Data Analytics & Machine Learning with Intel® Distribution for Python*

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ON DEMAND • 11:56 mins
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Article

K-means Acceleration with 2nd Generation Intel® Xeon® Scalable Processors

5/5 (1)
It's no secret that we produce extraordinary amounts of data every day, necessitating the need for performant, scalable algorithms that can process it all. Explore the range of Intel data analytics tools, all optimized for Big Data hardware and software stacks.
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Article

Measuring Graph Analytics Performance

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What is Graph Analytics and why does it matter? Short answer: Because it touches a huge swath of our daily lives. Find out how new Intel® tools simplify getting objective, reproducible graph analytics performance data without obfuscation.
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Big Picture Video

Graph Analytics: A Foundational Building Block for the Data Analytics World

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Essentials

Machine Learning 101 with Python and daal4py

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ON DEMAND • 55:35 mins
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Essentials

Improve Performance on Distributed Deep Learning Training Workloads

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ON DEMAND • 36:06 mins
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Article

Boosting the Performance of Graph Analytics Workloads

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There are a variety of algorithms to extract useful info from a graph. Explore the implementation characteristics of basic graph analysis algorithms and how they perform on Intel® Xeon® processors.
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Article

Parallelism in Python* Using Numba*

NAN/5 (1)
There are three ways to efficiently achieve parallelism in Python*: dispatch your own code, rely on a library, use a framework. This article looks at the third way … using Numba* to generate native-speed code.
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Article

Effectively Train and Execute Machine Learning and Deep Learning Projects on CPUs

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Meet Intel’s optimizations for frameworks like Caffe* and TensorFlow* and see how Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)helps speed training and inference execution with no code changes.
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Essentials

Get Deep Learning Framework Performance on Intel® Architecture

4.5/5 (2)
ON DEMAND • 61:29 mins
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