Nice post about how you can start solving ML tasks:
Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik
http://blog.kaggle.com/2017/03/24/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik/
#kaggle #ml #interview #novice2master
Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik
http://blog.kaggle.com/2017/03/24/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik/
#kaggle #ml #interview #novice2master
No Free Hunch
Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik
Can you see the random forest for its leaves? The Leaf Classification playground competition challenged Kagglers to correctly identify 99 classes of leaves [...]
Jonker-Volgenant Algorithm + t-SNE = Super Powers: https://blog.sourced.tech/post/lapjv/
#tsne #visualization
#tsne #visualization
Google has set up a new milestone for speech generation: "Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model"
You can listen to generated samples at: https://google.github.io/tacotron/
Paper: https://arxiv.org/abs/1703.10135
#audio #arxiv #google #breakthrough #generative
You can listen to generated samples at: https://google.github.io/tacotron/
Paper: https://arxiv.org/abs/1703.10135
#audio #arxiv #google #breakthrough #generative
arXiv.org
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires...
The Verge’s piece on how Alibaba uses AI to study playing Starcraft. Probably this is the way to attract new brilliant researches into the company, netherless, results look really promising.
http://www.theverge.com/2017/4/3/15164490/alibaba-ai-starcraft-combat
#reinforcementlearning #alibaba #starcraft
http://www.theverge.com/2017/4/3/15164490/alibaba-ai-starcraft-combat
#reinforcementlearning #alibaba #starcraft
The Verge
AI is one step closer to mastering StarCraft
Last year, Alphabet’s DeepMind division captured the world’s attention by besting humanity’s top player in the game of Go. The achievement, which many experts predicted was still a decade off,...
Tech Review on Facebook incredible chatbot:
https://www.technologyreview.com/s/604117/facebooks-perfect-impossible-chatbot/
#lyrics #mit #nlp #qa #chatbot
Facebook is quietly trying to develop the most useful virtual assistant ever, in a project that illustrates the current limitations of artificial intelligence.
https://www.technologyreview.com/s/604117/facebooks-perfect-impossible-chatbot/
#lyrics #mit #nlp #qa #chatbot
MIT Technology Review
Facebook’s Perfect, Impossible Chatbot
Facebook is quietly trying to develop the most useful virtual assistant ever, in a project that illustrates the current limitations of artificial intelligence.
The Conversational Intelligence Challenge
NIPS 2017 Live Competition
Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is closely related to the overall goal of AI. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations about specific news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.
Organizers
Mikhail Burtsev, Valentin Malykh, MIPT, Moscow
Ryan Lowe, McGill University, Montreal
Iulian Serban, Yoshua Bengio, University of Montreal, Montreal
Alexander Rudnicky, Alan W. Black, Carnegie Mellon University, Pittsburgh
http://convai.io
#nlp #qa #nips
NIPS 2017 Live Competition
Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is closely related to the overall goal of AI. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations about specific news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.
Organizers
Mikhail Burtsev, Valentin Malykh, MIPT, Moscow
Ryan Lowe, McGill University, Montreal
Iulian Serban, Yoshua Bengio, University of Montreal, Montreal
Alexander Rudnicky, Alan W. Black, Carnegie Mellon University, Pittsburgh
http://convai.io
#nlp #qa #nips
Outdated, but still valuable blog to dive into deep learning:
http://deeplearning.net/tutorial/
#tutorial #wheretostart
http://deeplearning.net/tutorial/
#tutorial #wheretostart
Nothing special, just siberian mathematicians went down to streets on national Labour Day holiday.
Spotify + Emoji = ❤️
https://insights.spotify.com/es/2017/05/02/spotify-emoji-music/
#insights #emoji #spotify
https://insights.spotify.com/es/2017/05/02/spotify-emoji-music/
#insights #emoji #spotify
Deep Learning Papers by task
https://github.com/sbrugman/deep-learning-papers
#deeplearning #list #github
https://github.com/sbrugman/deep-learning-papers
#deeplearning #list #github
GitHub
GitHub - sbrugman/deep-learning-papers: Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.
Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled. - sbrugman/deep-learning-papers
Using Deep Learning at Scale in Twitter’s Timelines
https://blog.twitter.com/2017/using-deep-learning-at-scale-in-twitter-s-timelines
https://blog.twitter.com/2017/using-deep-learning-at-scale-in-twitter-s-timelines
Twitter
Using Deep Learning at Scale in Twitter’s Timelines
Using Deep Learning at Scale in Twitter’s Timelines
Picasso: A free open-source visualizer for Convolutional Neural Networks:
https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5
#vizualization #deeplearning
https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5
#vizualization #deeplearning
Medium
Picasso: A free open-source visualizer for Convolutional Neural Networks
Cloudy with a chance of tanks
Data Science:
“Deep adversarial learning is finally ready 🚀 and will radically change the game”
https://medium.com/intuitionmachine/deep-adversarial-learning-is-finally-ready-and-will-radically-change-the-game-f0cfda7b91d3
“Deep adversarial learning is finally ready 🚀 and will radically change the game”
https://medium.com/intuitionmachine/deep-adversarial-learning-is-finally-ready-and-will-radically-change-the-game-f0cfda7b91d3
Medium
Deep adversarial learning is finally ready 🚀 and will radically change the game
Adversarial learning is one of the most hyped areas in deep learning. If you browse arxiv-sanity, you’ll notice much of the most popular…
Pix2code: Generating Code from a Graphical User Interface Screenshot. Front-end developers might start getting scared.
https://arxiv.org/abs/1705.07962
https://arxiv.org/abs/1705.07962
Microsoft released new version of High-Performance, Open-Source, Deep Learning Toolkit
https://news.developer.nvidia.com/microsoft-releases-new-version-of-high-performance-open-source-deep-learning-toolkit/
#microsoft #nvidia #news
https://news.developer.nvidia.com/microsoft-releases-new-version-of-high-performance-open-source-deep-learning-toolkit/
#microsoft #nvidia #news