http://www.news.cornell.edu/stories/2016/08/where-can-i-buy-chair-app-will-tell-you


Cornell University managed to release an app able to find common furniture online.

So if you see a sofa you like, you can just search for it online, to order the same in an instant.

Retailers' dream.

Apache Spark @Scale: A 60 TB+ production use case from Facebook.

https://databricks.com/blog/2016/08/31/apache-spark-scale-a-60-tb-production-use-case.html

This is next to HUGE.

Repo with PDF copy of http://www.deeplearningbook.org by Ian Goodfellow and Yoshua Bengio and Aaron Courville

https://github.com/HFTrader/DeepLearningBook

"Stacked Approximated Regression Machine: A Simple Deep Learning Approach"

https://arxiv.org/abs/1608.04062

Great article, you should definately check up

Video generation!

http://web.mit.edu/vondrick/tinyvideo/

Notes about CVPR 2016

https://gab41.lab41.org/five-things-i-learned-at-cvpr-2016-5e857c017f7b#.18bsihj9q

Flexible Image Tagging with Fast0Tag

https://gab41.lab41.org/flexible-image-tagging-with-fast0tag-681c6283c9b7#.bp7v0qdn7

And deep minds breakthrough in audio generation. It is able to generate human-like speech.

https://deepmind.com/blog/wavenet-generative-model-raw-audio/

If you know any fellow data scientists and machine learning guys, please spread the word and forward this message to them. We all work hard on providing you greatest news, so we will appreciate your attention.

How to write your first spam-filter yourself

https://blog.cambridgecoding.com/2016/01/25/implementing-your-own-spam-filter/

Two papers about using video games to train computer vision models.

And who says that video games are useless? 😏

https://arxiv.org/abs/1608.01745
https://arxiv.org/pdf/1608.01745v2.pdf

Machine learning algorithms

Language Model on One Billion Word Benchmark


In this release, we open source a model trained on the One Billion Word Benchmark (http://arxiv.org/abs/1312.3005), a large language corpus in English which was released in 2013. This dataset contains about one billion words, and has a vocabulary size of about 800K words. It contains mostly news data. Since sentences in the training set are shuffled, models can ignore the context and focus on sentence level language modeling.

In the original release and subsequent work, people have used the same test set to train models on this dataset as a standard benchmark for language modeling. Recently, we wrote an article (http://arxiv.org/abs/1602.02410) describing a model hybrid between character CNN, a large and deep LSTM, and a specific Softmax architecture which allowed us to train the best model on this dataset thus far, almost halving the best perplexity previously obtained by others.

Link for the repo: https://github.com/tensorflow/models/tree/master/lm_1b

Keras implementation of deepmind's wavenet paper

https://github.com/usernaamee/keras-wavenet

Generative Visual Manipulation on the Natural Image Manifold

For more details, please visit the project webpage:
https://people.eecs.berkeley.edu/~junyanz/projects/gvm/

"Generative Visual Manipulation on the Natural Image Manifold", Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros. In European Conference on Computer Vision (ECCV). 2016.

(via Deep Learning community on vk.com)

Convolutional Recurrent Neural Networks for Music Classification

https://keunwoochoi.wordpress.com/2016/09/15/paper-is-out-convolutional-recurrent-neural-networks-for-music-classification/

(again, via Deep Learning community on vk.com).

You can also use our @opendatasciencebot to submit an interesting link or get in contact with channel administration.

Fully-Convolutional Siamese Networks for Object Tracking

A new state-of-the-art for real-time tracking at 50-100 fps. It can be used to track objects in videos and stuff.

http://www.gitxiv.com/posts/TvEcWEJabGu7pEHEa/fully-convolutional-siamese-networks-for-object-tracking

Stanford University report on how life will be different with the AI by the 2030.
Spoiler: no skynet just yet.

https://ai100.stanford.edu/2016-report

There is an #opensource repository for automatic image captioning in #tensorflow
As article reports, researches have managed to significally improve quality of recognition.


https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html

#deeplearning

European companies involved with machine learning.

https://medium.com/project-juno/european-machine-intelligence-landscape-43a22b44e961