pix2code: Generating Code from a Graphical User Interface Screenshot
https://uizard.io/research#pix2code
https://uizard.io/research#pix2code
Сравнение Эльбрус-4С и Эльбрус-8С в нескольких задачах машинного зрения
https://habrahabr.ru/company/smartengines/blog/329858/
https://habrahabr.ru/company/smartengines/blog/329858/
Habr
Сравнение Эльбрус-4С и Эльбрус-8С в нескольких задачах машинного зрения
В этой статье мы покажем, как работают технологии распознавания образов на Эльбрус-4С и на новом Эльбрус-8С: рассмотрим несколько задач машинного зрения,...
О чем говорят женщины? (Text mining of beauty blogs)
https://habrahabr.ru/company/lanit/blog/329892/
https://habrahabr.ru/company/lanit/blog/329892/
habrahabr.ru
О чем говорят женщины? (Text mining of beauty blogs)
В руках нашей команды из CleverDATA оказался уникальный материал – около 100 тыс. страниц англоязычных блогов, посвященных бьюти-сфере. Этот корпус к нам попал...
MIPTDeepLearningLab/ConvAI-baseline
ConvAI (https://deeppavlov.github.io/convai/) baseline solution
https://github.com/MIPTDeepLearningLab/ConvAI-baseline
ConvAI (https://deeppavlov.github.io/convai/) baseline solution
https://github.com/MIPTDeepLearningLab/ConvAI-baseline
GitHub
MIPTDeepLearningLab/ConvAI-baseline
ConvAI-baseline - ConvAI baseline solution
Легкое погружение в TensorFlow на примерах популярных алгоритмов машинного обучения https://github.com/aymericdamien/TensorFlow-Examples
GitHub
GitHub - aymericdamien/TensorFlow-Examples: TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) - aymericdamien/TensorFlow-Examples
Туториал по работе с несбалансированными выборками
http://nbviewer.jupyter.org/github/Yorko/mlcourse_open/blob/master/jupyter_notebooks/tutorials/imbalanced_problems_labintcev.ipynb
http://nbviewer.jupyter.org/github/Yorko/mlcourse_open/blob/master/jupyter_notebooks/tutorials/imbalanced_problems_labintcev.ipynb
Рисуем интерактивные карты с Folium
http://nbviewer.jupyter.org/github/Yorko/mlcourse_open/blob/master/jupyter_notebooks/tutorials/folium_shlur.ipynb
http://nbviewer.jupyter.org/github/Yorko/mlcourse_open/blob/master/jupyter_notebooks/tutorials/folium_shlur.ipynb
Pytorch implementation of Grad-CAM
https://github.com/jacobgil/pytorch-grad-cam
https://github.com/jacobgil/pytorch-grad-cam
GitHub
GitHub - jacobgil/pytorch-grad-cam: Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification…
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more. - jacobgil/pytorch-grad-cam
Visualizing TensorFlow Graphs in Jupyter Notebooks
https://blog.jakuba.net/2017/05/30/tensorflow-visualization.html
https://blog.jakuba.net/2017/05/30/tensorflow-visualization.html
blog.jakuba.net
Visualizing TensorFlow Graphs in Jupyter Notebooks
Prerequisites: This article assumes you are familiar with the basics of Python, TensorFlow, and Jupyter notebooks. We won't use any of the advanced TensorFlo...
A deep dive into what AI actually means in 2017
https://blog.statsbot.co/3-types-of-artificial-intelligence-4fb7df20fdd8
https://blog.statsbot.co/3-types-of-artificial-intelligence-4fb7df20fdd8
Stats and Bots
A Big Data Cheat Sheet: From Narrow AI to General AI
Artificial Intelligence in 2017
Как научиться науке о данных (Data Science). И при этом не заплатив 150,000 рублей
http://spark-in.me/post/learn-data-science
http://spark-in.me/post/learn-data-science
From Instance Noise to Gradient Regularisation
http://www.inference.vc/from-instance-noise-to-gradient-regularisation/
http://www.inference.vc/from-instance-noise-to-gradient-regularisation/
inFERENCe
From Instance Noise to Gradient Regularisation
This is just a short note to highlight a new paper I read recently: Roth, Lucchi, Nowozin and Hofmann (2017) Stabilizing Training of Generative Adversarial Networks through Regularization Instance noise Last year, we and Arjovsky & Buttou (2017) independently…
spaCy's Machine Learning library for NLP in Python
https://github.com/explosion/thinc
https://github.com/explosion/thinc
GitHub
GitHub - explosion/thinc: 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries - explosion/thinc
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation
https://github.com/jacobgil/pytorch-explain-black-box
https://github.com/jacobgil/pytorch-explain-black-box
GitHub
jacobgil/pytorch-explain-black-box
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation - jacobgil/pytorch-explain-black-box
You can probably use deep learning even if your data isn't that big
http://beamandrew.github.io/deeplearning/2017/06/04/deep_learning_works.html
http://beamandrew.github.io/deeplearning/2017/06/04/deep_learning_works.html
WebDNN: JS Library to run DL models in browser. Optimized WebASM+GPU implementation.
https://mil-tokyo.github.io/webdnn/
https://mil-tokyo.github.io/webdnn/
mil-tokyo.github.io
MIL WebDNN
WebDNN is an open source software framework for fast execution of deep neural network (DNN) pre-trained model on web browser.
Visualizing the Latent Space of Vector Drawings from the Google QuickDraw Dataset with SketchRNN, PCA and t-SNE
http://louistiao.me/posts/notebooks/visualizing-the-latent-space-of-vector-drawings-from-the-google-quickdraw-dataset-with-sketchrnn-pca-and-t-sne/
http://louistiao.me/posts/notebooks/visualizing-the-latent-space-of-vector-drawings-from-the-google-quickdraw-dataset-with-sketchrnn-pca-and-t-sne/
"in the brain, such a representation is produced by an architecture similar to a hierarchical feedforward deep-network"
http://www.cell.com/cell/abstract/S0092-8674(17)30538-X
http://www.cell.com/cell/abstract/S0092-8674(17)30538-X