Graph shows what people really mean when they use vague terminology describing the probability of an event.

Baidu’s neural network based system learned to "clone" a voice with less than a minute of audio data from the speaker.

Explaining website: http://research.baidu.com/neural-voice-cloning-samples/
Paper: https://arxiv.org/pdf/1802.06006.pdf

#DeepLearning #Voice #Speech

«Efficient Neural Architecture Search via Parameters Sharing»

Authors reduced the computational requirement (GPU-hrs) of standard Neural Architecture Search by 1000x via parameter sharing between models that are subgraphs within a large computational graph. ENAS achieves SOTA on PTB language modeling among all methods without post-training processing and strong performance on CIFAR-10.

Link: https://arxiv.org/pdf/1802.03268.pdf

#arxiv #optimization #neuralnetworks

«A Closed-form Solution to Photorealistic Image Stylization» — new release by NVidia, exploring photorealistic style transfer.

The proposed algorithm consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step encourages spatially consistent stylizations.

Arxiv: https://arxiv.org/abs/1802.06474

Pictures: https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/alg_in_action.png

Most common libraries for Natural Language Processing:

CoreNLP from Stanford group:
http://stanfordnlp.github.io/CoreNLP/index.html

NLTK, the most widely-mentioned NLP library for Python:
http://www.nltk.org/

TextBlob, a user-friendly and intuitive NLTK interface:
https://textblob.readthedocs.io/en/dev/index.html

Gensim, a library for document similarity analysis:
https://radimrehurek.com/gensim/

SpaCy, an industrial-strength NLP library built for performance:
https://spacy.io/docs/

Source: https://itsvit.com/blog/5-heroic-tools-natural-language-processing/

#nlp #digest #libs

How to find out that there is something wrong with your child:

It is important not only to build and train a model, but also to serve it in the production. Thus, this article might be a great starting point:

Deep learning in production with Keras, Redis, Flask, and Apache

https://www.pyimagesearch.com/2018/02/05/deep-learning-production-keras-redis-flask-apache/?utm_source=opendatascience

#dl #deeplearning #keras #production

😂😭 like back to school

Google has released a new open and free machine learning course. The course is built around Google's TensorFlow API, which is now one step closer to becoming an industry standard.

https://developers.google.com/machine-learning/crash-course/
#mooc #course #google #tensorflow

The Verdict Is In: AI Outperforms Human Lawyers in Reviewing Legal Documents

Another vector for AI startups.

https://futurism.com/ai-contracts-lawyers-lawgeex/

Google has started its Large-Scale Landmark Recognition Challenge.

Consits of two Kallge competitions: https://www.kaggle.com/c/landmark-recognition-challenge and https://www.kaggle.com/c/landmark-retrieval-challenge.

https://landmarkscvprw18.github.io/

Notes by Tess Ferrandez on the Andrew Ng’s Deep learning specialization.

Very nice way to refresh you memory of DL concepts.

Credit: Tess Ferrandez, https://www.slideshare.net/TessFerrandez/notes-fr

Neural Voice Cloning with a Few Samples

Paper behind Baidu's Neural Voice Cloning with Few samples: http://research.baidu.com/neural-voice-cloning-samples/

Arxiv: https://arxiv.org/abs/1802.06006

#arxiv #baidu #neuralnetworks #voice #sound #dl

Semantic Image Segmentation with DeepLab in Tensorflow

https://research.googleblog.com/2018/03/semantic-image-segmentation-with.html

#tensorflow #google #deeplab #dl

“Listing Embeddings for Similar Listing Recommendations and Real-time Personalization in Search”

From #Airbnb team
https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e

Nature has published an article with a #superresolution approach for #CT scans.

https://www.sciencedaily.com/releases/2018/03/180321155324.htm
#arxiv: https://arxiv.org/abs/1704.08841

Comparison of gradient boosting algorithms: CatBoost vs. Light GBM vs. XGBoost

https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db

#xgboost #gbm

NIPS ConvAI2 competition!

http://convai.io

Train Dialogue Agents to chat about personal interests and get to know their dialogue partner -- using the PersonaChat dataset as a training source.

Competition starts now! Ends September 1st.

#nips #conversational #ai #competition

Another approach for writing scientific paper: https://pjreddie.com/media/files/papers/YOLOv3.pdf.

Less BS, more data and clear messages (like `What This All Means`).

Another quote:

YOLOv3 is a good detector. It’s fast, it’s accurate. It’s not as great on the COCO average AP between .5 and .95 IOU metric. But it’s very good on the old detection metric of .5 IOU.