Визуализации: кто такой Data Scientist и классификация алгоритмов Машинного Обучения.
LSTMVis - Visual Analysis for Recurrent Neural Networks
LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.
http://lstm.seas.harvard.edu/
LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.
http://lstm.seas.harvard.edu/
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
This project is a baseline in the activity classification and its temporal location, focused on the ActivityNet Challenge. Here is detailed all the process of our proposed pipeline, as well the trained models and the utility to classify and temporally localize activities on new videos given. All the steps have been detailed, from downloading the dataset, to predicting the temporal locations going through the feature extraction and also the training.
https://imatge-upc.github.io/activitynet-2016-cvprw/
https://github.com/imatge-upc/activitynet-2016-cvprw
https://www.youtube.com/watch?v=3G-Vdmsluw0
#keras #video #activity #cvprw
This project is a baseline in the activity classification and its temporal location, focused on the ActivityNet Challenge. Here is detailed all the process of our proposed pipeline, as well the trained models and the utility to classify and temporally localize activities on new videos given. All the steps have been detailed, from downloading the dataset, to predicting the temporal locations going through the feature extraction and also the training.
https://imatge-upc.github.io/activitynet-2016-cvprw/
https://github.com/imatge-upc/activitynet-2016-cvprw
https://www.youtube.com/watch?v=3G-Vdmsluw0
#keras #video #activity #cvprw
FACE DETECTION BY LITERATURE
Обзор и бенчмарки различных методов детектирования лиц
#facedetection #cv
http://www.erogol.com/face-detection-networks-literature/
Обзор и бенчмарки различных методов детектирования лиц
#facedetection #cv
http://www.erogol.com/face-detection-networks-literature/
Блог Kaggle: Что мы читаем, 15 Любимых Data Science ресурсов
http://blog.kaggle.com/2016/09/13/what-were-reading-data-science-resources/
http://blog.kaggle.com/2016/09/13/what-were-reading-data-science-resources/
Google, Facebook, Amazon объединяют силы для развития искусственного разума
http://www.bbc.com/russian/news-37503129
http://www.bbc.com/russian/news-37503129