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Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 138 823 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 817-o'rinni va Italiya mintaqasida 88-o'rinni egallagan.

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Postlar arxiv

138 823

Classification library https://github.com/Tessellate-Imaging/monk_v1 Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo Detection and Segmentation Library https://github.com/Tessellate-Imaging/ Monk_Object_Detection Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

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80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains 📌 Agriculture and Food 📌 Medical and Healthcare 📌 Satellite 📌 Security and Surveillance 📌 ADAS and Self Driving Cars 📌 Retail and E-Commerce 📌 Wildlife

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Blog: https://towardsdatascience.com/pp-yolo-surpasses-yolov4-object-detection-advances-1efc2692aa62 Github: https://github.com/PaddlePaddle/PaddleDetection

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Baidu publishes PP-YOLO and pushes the state of the art in object detection research.

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Tackled the problem of defining a perturbation set for real-world perturbations which cannot be easily described with a set of equations. Paper: https://arxiv.org/abs/2007.08450 Blog post: https://locuslab.github.io/2020-07-20-perturbation/ Code: https://github.com/locuslab/perturbation_learning

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Learning perturbation sets for robust machine learning

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https://laconicml.com/

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One more great source of Data Science, Deep Learning, Machine Learning, Computer Vision, AI and more... Enjoy with hot topics and projects

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https://github.com/rasbt/deeplearning-models

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Up-to-date and detailed explanation of Deep Learning Models from Sebastian Raschka A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. (80 Jupyter Notebook notes in total)

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https://arxiv.org/pdf/2003.11755.pdf

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Recently published Comprehensive survey about role of Deep Learning for Scientific discovery (March, 2020). Well structured information given from the authors by providing supplementary materials (Github code links). It worth to spend time to read.

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https://aihub.org/

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One more great website specialized to AI with News, Articles, Opinions, Tutorials, Resources and much more supported by Geeks of AI

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https://laconicml.com/free-artificial-intelligence-courses/?fbclid=IwAR2oQoGYdTqiQ6sOJC7LvJxpaeSVVBLwk6Fs7S-_p2iMDq-BqbrpqBUqUrc

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List some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where you live

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But, You can follow what happens there almost in real time: fill below link and receive every day during CVPR the official magazine CVPR Daily (16-17-18 June) - with all the highlights from CVPR, the Computer Vision and Pattern Recognition conference. https://www.rsipvision.com/feel-at-cvpr-as-if-you-were-at-cvpr/ Open Access version of papers are available at: http://openaccess.thecvf.com/CVPR2020.py

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It's CVPR time! We will not meet in person next week at CVPR 2020 Seattle: The conference has gone virtual...

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