- Researchers link compulsive Facebook checking to lack of sleep: Study correlates tiredness, crankiness, distractibility and social media browsing — ScienceDaily
- AO – Visualizing GoogLeNet Classes
- Kitten Bowl #kittenbowl | Hallmark Channel
- The Wrong Abstraction — Sandi Metz
RT @BatLabels: COFFEE
THE WORLD’S BEST WAY TO STAY AWAKE
- Propositions as Filenames, Builds as Proofs: The Essence of Make
- ciprian-chelba/1-billion-word-language-modeling-benchmark: Formerly known as code.google.com/p/1-billion-word-language-modeling-benchmark
The project makes available a standard corpus of reasonable size (0.8 billion words)
to train and evaluate language models.
A few sample results we obtained at Google on this data are detailed at:
Besides the scripts needed to rebuild the training/held-out data, it also makes
available log-probability values for each word in each of ten feld-out data sets,
for each of the following baseline models:
. unpruned Katz (1.1B n-grams),
. pruned Katz (~15M n-grams),
. unpruned Interpolated Kneser-Ney (1.1B n-grams),
. pruned Interpolated Kneser-Ney (~15M n-grams)
The corpus is derived from the training-monolingual.tokenized/news.20??.en.shuffled.tokenized data distributed at http://statmt.org/wmt11/translation-task.html, Monolingual language model training data (Download it all in one file, 11 GB, at http://statmt.org/wmt11/training-monolingual.tgz).
- Fuck Off As A Service (FOAAS)
- Kubernetes on Raspberry Pi
- Gender Bias In Hiring: Interviewing as a Trans Woman in Tech by February Keeney | Model View Culture
- Climate Economists React to Obama’s Proposed Oil Tax | Climate Central
RT @JossFong: i can understand people who are sad, angry, terrified, but BORED? it’s like dozing off on a rocketship
- random tweet — see what the world is thinking
- A Person Subject to Government Secrecy Rules Complains About Hillary Clinton’s Approach to Them – The Atlantic
- Release v1.2.0: Release 1.2.0 · GoogleCloudPlatform/PerfKitBenchmarker · GitHub
RT @P3rfguy: PerfKit Benchmarker release v1.2.0 is OUT.
Question: How do you benchmark Cloud?
- GitHub · Where software is built
… allows you to run a command and see what it does to your files without actually doing it! After reviewing the operations listed, you can then decide whether you really want these things to happen or not.
- Untitled (http://i.imgur.com/R46yDI6.jpg?platform=hootsuite)
RT @estherschindler: An accurate map of *every* city
- Mapping The Geography of 2015 Through Massive News Mining
- Image Recognition
In the last few years the field of machine learning has made tremendous progress on addressing these difficult problems. In particular, we’ve found that a kind of model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks — matching or exceeding human performance in some domains.
Researchers have demonstrated steady progress in computer vision by validating their work against ImageNet — an academic benchmark for computer vision. Successive models continue to show improvements, each time achieving a new state-of-the-art result: QuocNet, AlexNet, Inception (GoogLeNet), BN-Inception-v2. Researchers both internal and external to Google have published papers describing all these models but the results are still hard to reproduce. We’re now taking the next step by releasing code for running image recognition on our latest model, Inception-v3.
- [1512.00567] Rethinking the Inception Architecture for Computer Vision
- Tips on Reducing Cable and Phone Bills From Ethically Ambiguous Experts – The New York Times
- How to view a .mobi file on my iPad – Quora
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