29 May 2023
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Our solution is designed for companies who want to go beyond #appdevelopment, and take their products to the next level by automating their operations using AI/ML.
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4 Mar 2020
Under the hood, oneAPI provides an abstraction later to run code targeting heterogeneous compute. It will do the hard work, pulling primitives from the appropriate libraries for CPU or GPU.
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4 Mar 2020
MKL (Math Kernel Library) provides optimizations to run code efficiently on CPU. OpenVINO uses MKL and optimizes inference on target hardware: CPU, GPU, FPGA, or VPU.
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3 Mar 2020
Wow I'd missed that! Does that mean we can use MKL and have it automatically use an Intel GPU if available? Or a combination of CPU/GPU? Seen any benchmarks?
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3 Mar 2020
RT @AIMLDeveloper : As a result, everything converges to linguistics.
fastcompany.com/90470552/sur…
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3 Mar 2020
Of course, accessibility and ease of use determine success.
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8 Feb 2020
Just type in Google their names, download PDF and translate.
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16 Jan 2020
Use windows in advanced mode, uninstall non-system background services.... Custom algorithms. Don't trust Python ML frameworks.
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5 Jan 2020
25 Sep 2019
Our favorite PCA with kernel regression outperforms deep learning tests, RBF Net, and all others ARIMA etc. In the picture, one of the tests of a random combination of functions with randomly added distortions. But it's not difficult to try another dataset.
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5 Jan 2020
Uh, so why would anyone use linear activation functions in a neural net? That would defeat their purpose, wouldn't it?
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5 Jan 2020
Therefore, data cleaning, PCA, KPCA, good regression and quality data. And NN is a waste of time.
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26 Dec 2019
Edward O. Thorp
A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market?
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11 Dec 2019
RT @AIMLDeveloper : This is the meaning of ML, which was laid down by its creators: Vapnik and Chervonenskis. They worked at the Russian Academy of Sciences in conditions without the right to make a mistake. Actually, they formulated the concept of an ideal machine back in 1970.
7 Dec 2019
People are biased.
Data is biased, in part because people are biased.
Algorithms trained on biased data are biased.
But learning algorithms themselves are not biased.
Bias in data can be fixed.
Bias in people is harder to fix. nytimes.com/2019/12/06/busin…
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