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Netflix’s Polynote is a New Open Source Framework to Build Better Data Science Notebooks
https://www.kdnuggets.com/2020/08/netflix-polynote-open-source-framework-better-data-science-notebooks.html
Project page: https://polynote.org/
Github: https://github.com/polynote/polynote
@Machine_learn
https://www.kdnuggets.com/2020/08/netflix-polynote-open-source-framework-better-data-science-notebooks.html
Project page: https://polynote.org/
Github: https://github.com/polynote/polynote
@Machine_learn
Israeli Geometric Deep Learning Workshop
Many cool presentations at the recent DGL workshop, including Yaron Lipman, Gal Chechik, Gal Chechik, and many other experienced people in this field. The video is on YouTube.
@Machine_learn
Many cool presentations at the recent DGL workshop, including Yaron Lipman, Gal Chechik, Gal Chechik, and many other experienced people in this field. The video is on YouTube.
@Machine_learn
YouTube
iGDL 2020: Israeli Geometric Deep Learning Workshop
We are excited to announce the first Israeli workshop on geometric deep learning (iGDL) that will take place on August 2nd, 2020 2 PM-7 PM (Israel timezone). The workshop will be in English, and will take place virtually via Zoom due to COVID19 restrictions.…
Data_Analytics_Practical_Guide_to_Leveraging_the_Power_of_Algorithms.pdf
1.2 MB
Data Analytics
Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life
#book
@Machine_learn
Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life
#book
@Machine_learn
How to Build a Custom YOLOv4 Object Detector using TensorFlow
@Machine_learn
https://morioh.com/p/f9702a8223b2
CODE: https://github.com/theAIGuysCode/tensorflow-yolov4-tflite
@Machine_learn
https://morioh.com/p/f9702a8223b2
CODE: https://github.com/theAIGuysCode/tensorflow-yolov4-tflite
Using Flask to optimize performance with Mask R-CNN segmentation(with source code)
https://medium.com/medialesson/using-flask-to-optimize-performance-with-mask-r-cnn-segmentation-39752f153029
@Machine_learn
https://medium.com/medialesson/using-flask-to-optimize-performance-with-mask-r-cnn-segmentation-39752f153029
@Machine_learn
Medium
Using Flask to optimize performance with Mask R-CNN segmentation
How to improve Mask R-CNN segmentation performance using a Flask web service.
OpenCV Sudoku Solver and OCR
https://www.pyimagesearch.com/2020/08/10/opencv-sudoku-solver-and-ocr/
@Machine_learn
https://www.pyimagesearch.com/2020/08/10/opencv-sudoku-solver-and-ocr/
@Machine_learn
PyImageSearch
OpenCV Sudoku Solver and OCR - PyImageSearch
In this tutorial, you will create an automatic sudoku puzzle solver using OpenCV, Deep Learning, and Optical Character Recognition (OCR).
2_Improving_Deep_Neural_Networks.pdf
992.8 KB
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
@Machine_learn
@Machine_learn
Top 20+ highly ranked Coursera Courses for Data Science & Machine Learning beginners and advanced
@Machine_learn
https://nuggetsnetwork.com/blog/Top-Coursera-DataScience-Courses.html
@Machine_learn
https://nuggetsnetwork.com/blog/Top-Coursera-DataScience-Courses.html
Nuggets Network
https://nuggetsnetwork.com/
Top 20 ranked Best Data Science & Machine Learning Courses from Coursera [2020]
@Machine_learn
Axial-DeepLab: Long-Range Modeling in All Layers for Panoptic Segmentation
https://ai.googleblog.com/2020/08/axial-deeplab-long-range-modeling-in.html
Axial-DeepLab: Long-Range Modeling in All Layers for Panoptic Segmentation
https://ai.googleblog.com/2020/08/axial-deeplab-long-range-modeling-in.html
research.google
Axial-DeepLab: Long-Range Modeling in All Layers for Panoptic Segmentation
Posted by Huiyu Wang, Student Researcher and Yukun Zhu, Software Engineer, Google Research The success of convolutional neural networks (CNNs) main...
Documentation:
1) https://bigml.com/developers
2) https://predictionio.apache.org/datacollection/eventapi/
3) https://docs.anaconda.com/
4) https://github.com/blue-yonder
5) https://docs.mljar.com/
6) https://nupic.docs.numenta.org/
7) https://docs.recombee.com/
8) https://indico.io/docs/
9) https://api.animetrics.com/documentation
10) https://face.eyedea.cz:8080/api/face/docs
11) https://www.betafaceapi.com/wpa/index.php/documentation
12) https://docs.imagga.com/
13) https://wit.ai/docs
14) https://docs.api.bitext.com/
15) https://api.geneea.com/
16) https://www.diffbot.com/dev/docs/
17) https://yactraq.com/contact-trial/
18) https://monkeylearn.com/api/v3/
19) https://help.hutoma.ai/article/ym34wr87lx-hutoma-chat-api
20) https://php-nlp-tools.com/documentation/
@Machine_learn
1) https://bigml.com/developers
2) https://predictionio.apache.org/datacollection/eventapi/
3) https://docs.anaconda.com/
4) https://github.com/blue-yonder
5) https://docs.mljar.com/
6) https://nupic.docs.numenta.org/
7) https://docs.recombee.com/
8) https://indico.io/docs/
9) https://api.animetrics.com/documentation
10) https://face.eyedea.cz:8080/api/face/docs
11) https://www.betafaceapi.com/wpa/index.php/documentation
12) https://docs.imagga.com/
13) https://wit.ai/docs
14) https://docs.api.bitext.com/
15) https://api.geneea.com/
16) https://www.diffbot.com/dev/docs/
17) https://yactraq.com/contact-trial/
18) https://monkeylearn.com/api/v3/
19) https://help.hutoma.ai/article/ym34wr87lx-hutoma-chat-api
20) https://php-nlp-tools.com/documentation/
@Machine_learn
1. Cassie Kozyrkov : https://www.linkedin.com/in/cassie-kozyrkov-9531919/
• Medium : https://medium.com/@kozyrkov
2. Ben Taylor : https://www.linkedin.com/in/bentaylordata/
3. Dat Tran : https://www.linkedin.com/in/dat-tran-a1602320/
4. Ian Goodfellow : https://www.linkedin.com/in/ian-goodfellow-b7187213
5. Jose Marcial Portilla : https://www.linkedin.com/in/jmportilla/
6. Koo Ping Shung : https://www.linkedin.com/in/koopingshung/
7. Lex Fridman : https://www.linkedin.com/in/lexfridman/
8. Kristen Kehrer : https://www.linkedin.com/in/kristen-kehrer-datamovesme/
9. Srivatsan Srinivasan : https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/
10. Andrew Ng : https://www.linkedin.com/in/andrewyng
@Machine_learn
• Medium : https://medium.com/@kozyrkov
2. Ben Taylor : https://www.linkedin.com/in/bentaylordata/
3. Dat Tran : https://www.linkedin.com/in/dat-tran-a1602320/
4. Ian Goodfellow : https://www.linkedin.com/in/ian-goodfellow-b7187213
5. Jose Marcial Portilla : https://www.linkedin.com/in/jmportilla/
6. Koo Ping Shung : https://www.linkedin.com/in/koopingshung/
7. Lex Fridman : https://www.linkedin.com/in/lexfridman/
8. Kristen Kehrer : https://www.linkedin.com/in/kristen-kehrer-datamovesme/
9. Srivatsan Srinivasan : https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/
10. Andrew Ng : https://www.linkedin.com/in/andrewyng
@Machine_learn
Introducing Opacus: A high-speed library for training PyTorch models with differential privacy
https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy/
Github: https://github.com/pytorch/opacus
Differential Privacy Series Part 1 | DP-SGD Algorithm Explained: https://medium.com/pytorch/differential-privacy-series-part-1-dp-sgd-algorithm-explained-12512c3959a3
@Machine_learn
https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy/
Github: https://github.com/pytorch/opacus
Differential Privacy Series Part 1 | DP-SGD Algorithm Explained: https://medium.com/pytorch/differential-privacy-series-part-1-dp-sgd-algorithm-explained-12512c3959a3
@Machine_learn
Meta
Introducing Opacus: A high-speed library for training PyTorch models with differential privacy
We are releasing Opacus, a new high-speed library for training PyTorch models with differential privacy (DP) that’s more scalable than existing state-of-the-art methods.