GPUs are now available for Google Compute Engine and Cloud Machine Learning

@tachyeonz : The new Google Cloud GPUs are tightly integrated with Google Cloud Machine Learning (Cloud ML), helping you slash the time it takes to train machine learning models at scale using the TensorFlow framework.

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Preprocessing for Machine Learning with tf.Transform

@tachyeonz : When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks.

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DeepMind just published a mind blowing paper: PathNet.

@tachyeonz : Potentially describing how general artificial intelligence will look like. Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck.

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Announcing TensorFlow Fold: Deep Learning With Dynamic Computation Graphs

@tachyeonz : In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches.

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Personalization and Scalable Deep Learning with MXNET

@tachyeonz : The presentation below by Alex Smola is “Personalization and Scalable Deep Learning with MXNET” from the MLconf San Francisco, 2016. User return times and movie preferences are inherently time dependent.

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This Is How Quantum Computing Will Change The World | Erick Ladizinsky – D Wave Founder

@tachyeonz : The Coming Quantum Computing Revolution – Eick LadizinskyClick here to subscribe to Futurology on YouTube: http://bit.ly/2kcLe7F other videos :The Future of Nanotechnology : https://www.youtube.com/watch?v=VZbCx…What is the fastest way way to reach mars ? : https://www.youtube.com/watch?v=Lqz

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Google RAISR Intelligently Makes Low-Res Images High Quality

@tachyeonz : With unlimited data plans becoming increasingly expensive, or subscribers being forced to ditch their unlimited data due to overuse, anything that can reduce the amount of data we download is welcome.

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Some autonomous cars will avoid being hacked by simply staying off the Internet

@tachyeonz : Self-driving cars are essentially large computers on wheels. No surprise, then, that some of them will be kept offline in the name of security.

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Quantum Computing Is Real, and D-Wave Just Open-Sourced It

@tachyeonz : Quantum computing is real. But it’s also hard. So hard that only a few developers, usually trained in quantum physics, advanced mathematics, or most likely both, can actually work with the few quantum computers that exist.

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Ian Goodfellow, Google – Practical Methodology for Deploying Machine Learning #AIWTB Oct 2015

@tachyeonz : Ian Goodfellow, Google Research Scientist’s talk for the 1st edition of AI With The Best online conference Oct 2015 on Practical Methodology for Deploying Machine Learning. To obtain good performance from a machine learning application, it is important to choose the right machine learning model, alg

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Published On:January 05, 2017 at 06:38PM

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Quantum computers ready to leap out of the lab in 2017

@tachyeonz : Quantum computing has long seemed like one of those technologies that are 20 years away, and always will be. But 2017 could be the year that the field sheds its research-only image.

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Tags : d wave, google, ionq, microsoft, quantum circuits, quantum computer, quantum computing, qubits, research, rigetti, superconductivity, topological quantum, z

Published On:January 05, 2017 at 02:05AM

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Deep Learning Race: A Survey of Industry Players’ Strategies

@tachyeonz : I’ve been working for quite a while now in trying to make sense of the research developments in Deep Learning. The methodology I’ve employed is through the cataloging of Design Patterns. It’s been quite effective in disentangling this ever growing complex field.

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Tags : amazon, apple, applications, baidu, companies, competition, data science, deep learning, facebook, google, innovations, m, microsoft, neural networks, research, strategy, technology, tesla, twitter, uber, use cases

Published On:December 29, 2016 at 11:54PM

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