Machine learning books and papers

Link : bit.ly/fund_dataProc @Machine_learn

LaSOT Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance. http://vision.cs.stonybrook.edu/~lasot/ Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html Paper: https://arxiv.org/abs/2009.03465 @Machine_learn

‍ Must Download : CheatSheet Collection For Data Science in ZIP Total Folder - 22 Total Size - 216 MB - Artificial Intelligence - Machine learning - Big Data - OpenCV CheetSheet - Dev Ops - Data Analytics - Python Cheetsheet - Mathematics - Excel - Probability - SQL - Statistics - Deep learning - Data Warehouse - Linux - Interview Question - Docker & Kubernetes - Matlab & R Cheatsheet - Scala CheetSheet @Machine_learn

This AI Creates Human Faces From Your Sketches! https://www.youtube.com/watch?v=5NM_WBI9UBE Paper: https://arxiv.org/abs/2006.01047 @Machine_learn

🧙‍♂️ How to Create a Cartoonizer with TensorFlow Lite https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html Code: https://github.com/margaretmz/cartoonizer-with-tflite E2E TFLite Tutorials: https://github.com/ml-gde/e2e-tflite-tutorials @Machine_learn

MushroomRL Reinforcement Learning Python library Github: https://github.com/MushroomRL/mushroom-rl Project page: https://github.com/openai/mujoco-py @Machine_learn

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial 👉👉 Watch Here 👉👉 https://youtu.be/tPYj3fFJGjk ⭐️ About the Author ⭐️ The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below: 🔗 YouTube: https://www.youtube.com/channel/UC4JX... 🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/ ⭐️ Course Contents ⭐️ ⌨️ Module 1: Machine Learning Fundamentals (00:03:25) ⌨️ Module 2: Introduction to TensorFlow (00:30:08) ⌨️ Module 3: Core Learning Algorithms (01:00:00) ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39) ⌨️ Module 5: Deep Computer Vision - Convolutional Neural Networks (03:43:10) ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44) ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00) ⌨️ Module 8: Conclusion and Next Steps (06:48:24) TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial @Machine_learn

The Little W-Net that Could State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models. Github: https://github.com/agaldran/lwnet Paper: https://arxiv.org/abs/2009.01907v1 @Machine_learn

Machine learning – Linear Regression Course (Free) . Linear regression is perhaps one of the most popular and widely used algorithms in statistics and machine learning. . Link : https://bit.ly/31W6yH1 @Machine_learn

https://github.com/facebookresearch/fairmotion https://research.fb.com/publications/a-scalable-approach-to-control-diverse-behaviors-for-physically-simulated-characters/ @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

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

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) http://nupic.docs.numenta.org/ 7) https://docs.recombee.com/ 8) https://indico.io/docs/ 9) http://api.animetrics.com/documentation 10) http://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) http://php-nlp-tools.com/documentation/ @Machine_learn

@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

Free course on Data Visualisation Methods @Machine_learn Link : bit.ly/2XY4Suw

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