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mixup: Data-Dependent Data Augmentation

转载自:https://www.inference.vc/mixup-data-dependent-data-augmentation/

Ferenc Huszar


发表于 2017-11-02

By popular demand, here is my post on mixup, a new data augmentation scheme that was shown to improve generalization and stabilize GAN performance.

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Gsutil cheatsheet

转载自:https://alexisperrier.com/gcp/2017/11/02/gsutil-cheatsheet.html

未知


发表于 2017-11-02

gsutil is Google Storage CLI tool. Equivalent to aws s3 but for the Google Cloud Platform, it allows you to access Google Cloud Storage from the command line. Beyond moving files and managing buckets, gsutil is a powerful file management (rsync) and file publication tool (signed urls).

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Self-Organizing Maps Tutorial

转载自:https://algobeans.com/2017/11/02/self-organizing-map/

Annalyn Ng


发表于 2017-11-02

The term ‘self-organizing map’ might conjure up a militaristic image of data points marching towards their contingents on a map, which is a rather apt analogy of how the algorithm actually works.

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PokerBot: Create your poker AI bot in Python

转载自:https://www.data-blogger.com/2017/11/01/pokerbot-create-your-poker-ai-bot-in-python/

Kevin Jacobs


发表于 2017-11-01

In this tutorial, you will learn step-by-step how to implement a poker bot in Python.

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Some comments to Daniel Abadi's blog about Apache Arrow

转载自:http://wesmckinney.com/blog/arrow-columnar-abadi/

Wes McKinney


发表于 2017-11-01

** Wed 01 November 2017

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Two Recent Results in Transfer Learning for Music and Speech

转载自:http://deliprao.com/archives/251

Delip


发表于 2017-11-01

In this post, I want to highlight two recent complementary results on transfer learning applied to audio — one related to music, another related to speech.

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New in Cloudera Data Science Workbench 1.2: Usage Monitoring for Administrators

转载自:http://blog.cloudera.com/blog/2017/10/new-in-cloudera-data-science-workbench-1-2-usage-monitoring-for-administrators/

Cy Jervis


发表于 2017-10-31

Cloudera Data Science Workbench (CDSW) provides data science teams with a self-service platform for quickly developing machine learning workloads in their preferred language, with secure access to enterprise data and simple provisioning of compute. Individuals can request schedulable resources (e.g. compute, memory, GPUs) on a shared cluster that is managed centrally.

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Recommender System

转载自:http://ramok.tech/2017/10/30/recommender-system/

Klevis Ramo


发表于 2017-10-30

In this post we are going to develop a movie recommender java application using Collaborative Filtering Algorithm.  Full code and working application are provided together with results.Generally no deep knowledge on the field is required and topics are kept as high level as possible.

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A Visual Guide to Evolution Strategies

转载自:http://blog.otoro.net/2017/10/29/visual-evolution-strategies/

未知


发表于 2017-10-29

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Weekly Review: 10/28/2017

转载自:https://codesachin.wordpress.com/2017/10/29/weekly-review-10282017/

srjoglekar246


发表于 2017-10-28

This was a pretty busy week with a lot going on, but I finally seem to be settling into my new role!

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