Catégorie : Machine Learning
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Uber Introduces AresDB: GPU-Powered, Open-Source, Real-Time Analytics Engine
https://www.infoq.com/news/2019/02/uber-aresdb-analytics
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Google Research: BERT, or Bidirectional Encoder Representations from Transformers
BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here: https://arxiv.org/abs/1810.04805. https://github.com/google-research/bert/blob/master/README.md
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Open-sourcing PyText for faster NLP development
To make it easier to build and deploy natural language processing (NLP) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale deployment. PyText is a library built on PyTorch, our unified, open source deep learning framework. https://code.fb.com/ai-research/pytext-open-source-nlp-framework/
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Google’s on-device text classification AI achieves 86.7% accuracy | VentureBeat
In a paper presented this week at the Conference on Empirical Methods in Natural Language Processing in Brussels, Belgium, Google researchers described offline, on-device AI systems — Self-Governing Neural Networks (SGNNs) — that achieve state-of-the-air results in specific dialog-related tasks. “The main challenges with developing and deploying deep neural network models on-device are (1) the…
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Startup’s AI Chip Beats GPU
Habana outruns Nvidia in inference https://www.eetimes.com/document.asp?doc_id=1333719
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Machine Learning cheatsheets for Stanford’s CS 229
Afshine Amidi (Ecole Centrale Paris, MIT) et Shervine Amidi (Ecole Centrale Paris, Stanford University) nous offre ici la traduction en français des cheatsheet du cours de machine learning de Stanford (https://stanford.edu/%7Eshervine/teaching/cs-229.html) https://github.com/afshinea/stanford-cs-229-machine-learning
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How is GloVe different from word2vec?
https://www.quora.com/How-is-GloVe-different-from-word2vec How is GloVe different from word2vec? – Quora www.quora.com Both models learn geometrical encodings (vectors) of words from their co-occurrence information (how frequently they appear together in large text corpora). They differ in that word2vec is a « predictive » model, whereas GloVe is a « count-based » mod…
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The 50 Best Free Datasets for Machine Learning – Gengo AI
https://gengo.ai/datasets/the-50-best-free-datasets-for-machine-learning/
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GPU-Enabled Docker Container
https://www.nvidia.com/object/docker-container.html
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100 Times Faster Natural Language Processing in Python
https://medium.com/huggingface/100-times-faster-natural-language-processing-in-python-ee32033bdced
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Machines Learn
https://www.reddit.com/r/MachinesLearn/
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Anatomy of an AI System
The Amazon Echo as an anatomical map of human labor, data and planetary resources By Kate Crawford 1 and Vladan Joler 2 (2018) https://anatomyof.ai/
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GitHub – GeniSysAI/NLU
The GeniSys NLU Engine includes a combination of a custom trained DNN (Deep Learning Neural Network) built using TFLearn for intent classification, and a custom trained MITIE model for entity classification. https://github.com/GeniSysAI/NLU
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https://medium.com/analytics-vidhya/25-fun-questions-for-a-machine-learning-interview-373b744a4faa
https://medium.com/analytics-vidhya/25-fun-questions-for-a-machine-learning-interview-373b744a4faa
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The Most Important Machine Learning Algorithms
https://semanti.ca/blog/?the-most-important-machine-learning-algorithms
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The Life of a Data Scientist Infographic
https://blog.cloudfactory.com/life-of-data-scientist?utm_content=74885902&utm_medium=social&utm_source=linkedin
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Google AI Blog: Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research
https://ai.googleblog.com/2018/08/introducing-new-framework-for-flexible.html?m=1
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How to solve 90% of NLP problems: a step-by-step guide
https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e
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Text classification · fastText
https://fasttext.cc/docs/en/supervised-tutorial.html
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Analytics API and Customer Data Platform · Segment
https://segment.com/
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La programmation de AI Fest 2018 prise en notes ! | Element AI
https://www.elementai.com/fr/news/2018/all-the-notes-from-ai-fest-2018
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Binarized Neural Networks
Paper Explanation: Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1 https://mohitjain.me/2018/07/14/bnn/ Paper Explanation: Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1 mohitjain.me Motivation What if you want to do some real-time face detection/recognition using a deep learning system running on a…
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tensorflow/magenta · GitHub
https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization
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GitHub – EvilPort2/PhotoStylist: Styling your photos with neural style transfer.
https://github.com/evilport2/PhotoStylist
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Visionner « TensorFlow Jumpstart » sur YouTube
https://youtu.be/DAt8xZQ9rZU
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New approximate nearest neighbor benchmarks
[browser-shot url= »https://erikbern-com.cdn.ampproject.org/c/s/erikbern.com/amp/2018/06/17/new-approximate-nearest-neighbor-benchmarks.html » width= »300″ height= »225]https://erikbern-com.cdn.ampproject.org/c/s/erikbern.com/amp/2018/06/17/new-approximate-nearest-neighbor-benchmarks.html[/browser-shot]
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Awesome Tensorlayer – A curated list of dedicated resources
https://github.com/tensorlayer/awesome-tensorlayer tensorlayer/awesome-tensorlayer github.com awesome-tensorlayer – TensorLayer – A curated list of dedicated resources
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Using ML.NET – Introduction to Machine Learning and ML.NET | Rubik’s Code
https://rubikscode.net/2018/06/18/using-ml-net-introduction-to-machine-learning-and-ml-net/
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Artificial Intelligence International Conference Series I PremC
http://premc.org/conferences/a2ic-artificial-intelligence/?utm_source=Twitter&utm_medium=cpc&utm_campaign=A2IC_2018&utm_term=abo.balajiln&utm_content=Banner
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