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https://github.com/mheriyanto/machine-learning-in-computer-vision
:memo: References list for machine learning and deep learning in computer vision.
https://github.com/mheriyanto/machine-learning-in-computer-vision
artificial-intelligence computer-vision deep-learning machine-learning python pytorch roadmap tensorflow
Last synced: 3 months ago
JSON representation
:memo: References list for machine learning and deep learning in computer vision.
- Host: GitHub
- URL: https://github.com/mheriyanto/machine-learning-in-computer-vision
- Owner: mheriyanto
- Created: 2019-06-01T04:52:46.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-08-01T06:53:07.000Z (almost 2 years ago)
- Last Synced: 2024-03-21T15:08:19.024Z (3 months ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, machine-learning, python, pytorch, roadmap, tensorflow
- Language: TeX
- Homepage: https://mheriyanto.wordpress.com
- Size: 13.4 MB
- Stars: 111
- Watchers: 7
- Forks: 40
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Lists
- data-science-awesome-reference - Machine Learning, Deep Learning, and Computer Vision References
README
## Machine Learning and Deep Learning in Computer Vision
> Data is [the new oil](https://medium.com/project-2030/data-is-the-new-oil-a-ludicrous-proposition-1d91bba4f294)? No: Data is [the new soil](https://towardsdatascience.com/data-is-not-the-new-oil-bdb31f61bc2d). ~ David McCandless
[](https://awesome.re)
[](https://github.com/mheriyanto/Data-Science/issues)
[](http://hits.dwyl.com/mheriyanto/Data-Science)
[](https://id.linkedin.com/in/mheriyanto)
:star: - Recommendations for Beginners.
## Awesome Lists
**Artificial Intelligence**
+ :star: [Awesome Artificial Intelligence - Lightman Wang](https://github.com/hades217/awesome-ai)  (General)
+ [Awesome Artificial Intelligence (AI) - Owain Lewis](https://github.com/owainlewis/awesome-artificial-intelligence)  (General)
+ [practicalAI - practicalAI](https://github.com/practicalAI/practicalAI) 
+ A list of artificial intelligence tools you can use today - for: [1. Personal use](https://medium.com/@Liamiscool/a-list-of-artificial-intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f), [2. Business use — Enterprise Intelligence](https://medium.com/@Liamiscool/a-list-of-artificial-intelligence-tools-you-can-use-today-for-businesses-2-3-eea3ac374835), [2. Business use (cont’d) — Enterprise Functions](https://medium.com/@Liamiscool/a-list-of-artificial-intelligence-tools-you-can-use-today-for-businesses-2-3-continued-21bf14280250), and [3. Industry specific businesses](https://medium.com/@Liamiscool/a-list-of-artificial-intelligence-tools-you-can-use-today-for-industry-specific-3-3-5e16c68da697)
+ FirmAI - [ML and DS Applications in **Industry**](https://github.com/firmai/industry-machine-learning)  | [ML and DS Applications in **Business**](https://github.com/firmai/business-machine-learning)  | [ML and DS Applications in **Asset Management**](https://github.com/firmai/machine-learning-asset-management)  | [ML and DS Applications in **Financial**](https://github.com/firmai/financial-machine-learning) 
**Machine Learning**
+ :star: [Machine Learning Mastery - Jason Brownlee](https://machinelearningmastery.com/start-here/) (General)
+ :star: [Homemade Machine Learning - Oleksii Trekhleb](https://github.com/trekhleb/homemade-machine-learning)  (Tutorial)
+ [2020 Machine Learning Roadmap](https://github.com/mrdbourke/machine-learning-roadmap)  (Roadmap)
+ [Python Machine Learning Jupyter Notebooks](https://github.com/tirthajyoti/Machine-Learning-with-Python)  (Tutorial)
+ [Machine Learning Mindset](https://github.com/machinelearningmindset) (Roadmap)
+ :star: [Awesome Machine Learning - Joseph Misiti](https://github.com/josephmisiti/awesome-machine-learning) 
+ [3D Machine Learning - Yuxuan (Tim) Zhang](https://github.com/timzhang642/3D-Machine-Learning) 
+ [Machine Learning Interviews from FAAG, Snapchat, LinkedIn - Khang Pham](https://github.com/khangich/machine-learning-interview) 
+ **Others**:
+ [mlcourse.ai - Yorko](https://github.com/Yorko/mlcourse.ai) 
+ [machine learning examples - lazyprogrammer](https://github.com/lazyprogrammer/machine_learning_examples) 
+ [ML From Scratch - eriklindernoren](https://github.com/eriklindernoren/ML-From-Scratch) 
**Deep Learning**
+ :star: [Awesome Deep Learning - Christos Christofidis](https://github.com/ChristosChristofidis/awesome-deep-learning)  (General)
+ [Awesome AutoDL - D-X-Y](https://github.com/D-X-Y/Awesome-AutoDL)  (General)
+ :star: [Deep Learning Papers Reading Roadmap - Flood Sung](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)  (Roadmap)
+ [Awesome Deep Learning Resources - Guillaume Chevalier](https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources)  (General)
+ :star: [Deep Learning Drizzle - Mario](https://github.com/kmario23/deep-learning-drizzle)  (Lecturers)
+ [Deep Learning with Python Notebooks](https://github.com/fchollet/deep-learning-with-python-notebooks)  (Tutorial)
+ [Awesome Deep Learning for Video Analysis - Huaizheng](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis)  (General)
+ [Awesome 3D Point Cloud Analysis - Yongcheng](https://github.com/Yochengliu/awesome-point-cloud-analysis)  (Roadmap)
+ [Awesome Tiny Object Detection](https://github.com/knhngchn/awesome-tiny-object-detection)  (General)
+ **Edge Detection**:
+ [Awesome Edge Detection Papers - Haoran MO](https://github.com/MarkMoHR/Awesome-Edge-Detection-Papers) 
+ [Awesome Background Subtraction - Murari Mandal](https://github.com/murari023/awesome-background-subtraction) 
+ [Awesome Semantic Segmentation - mrgloom](https://github.com/mrgloom/awesome-semantic-segmentation) 
+ **Object Detection and Tracking**:
+ :star: [Deep Learning Object Detection - Lee hoseong](https://github.com/hoya012/deep_learning_object_detection)  (Roadmap)
+ [Deep Learning for Tracking and Detection - Abhineet Singh](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection)  (Roadmap)
+ [Anomaly Detection Resources - Yue Zhao](https://github.com/yzhao062/anomaly-detection-resources)  (General)
+ [Awesome Anomaly Detection - Lee hoseong](https://github.com/hoya012/awesome-anomaly-detection)  (General)
**Computer Vision**
+ :star: [Awesome Computer Vision - Jia-Bin Huang](https://github.com/jbhuang0604/awesome-computer-vision)  (General)
+ [Awesome Deep Vision - Jiwon Kim](https://github.com/kjw0612/awesome-deep-vision)  (General)
+ :star: [Learn OpenCV - Satya Mallick](https://github.com/spmallick/learnopencv)  (Tutorial)
**Production**
+ Papers and blogs by organizations sharing their work on data science & machine learning in **production**: [applied-ml - eugeneyan](https://github.com/eugeneyan/applied-ml) 
+ [Deep-Learning-in-Production - ahkarami](https://github.com/ahkarami/Deep-Learning-in-Production): share some useful notes and references about deploying deep learning-based models in production. 
+ [Awesome MLOps](https://github.com/visenger/awesome-mlops): A curated list of references for MLOps. 
+ :star: [Awesome MLOps](https://github.com/kelvins/awesome-mlops): A curated list of awesome MLOps tools. 
**Compilers**
+ Awesome machine learning for **compilers** and program **optimisation**: [zwang4](https://github.com/zwang4/awesome-machine-learning-in-compilers) 
## Concepts
**Mathematics Concepts**
+ **ProofWiki** (proofwiki.org): [**Web**](https://proofwiki.org/wiki/Main_Page)
+ **Book of Proof** (Richard Hammack, 2018, 3rd Ed.): [**Book**](https://www.people.vcu.edu/~rhammack/BookOfProof/Main.pdf) | [**Web**](https://www.people.vcu.edu/~rhammack/BookOfProof/)
+ **Book of Proofs** (bookofproofs.org): [**Web**](https://www.bookofproofs.org/)
**Machine Learning Concepts**
+ :star: **Pengenalan Pembelajaran Mesin dan Deep Learning** (J.W.G. Putra, 2019): [**Book**](https://wiragotama.github.io/resources/ebook/intro-to-ml-secured.pdf) | [**GitHub**](https://github.com/wiragotama) | [**Web**](https://wiragotama.github.io/)
+ **Machine Learning Probabilistic Prespective** (K.P. Murphy, 2012. The MIT Press): [**Book**](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020) | [**GitHub**](https://github.com/probml/pyprobml) | [**Solution**](https://github.com/ArthurZC23/Machine-Learning-A-Probabilistic-Perspective-Solutions) | [**Web**](https://www.cs.ubc.ca/~murphyk/MLbook/)
+ **Pattern Recognition and Machine Learning** (C.M. Bishop. 2006. Springer): [**Book**](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) | [**GitHub**](https://github.com/ctgk/PRML) | [**Web**](https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/)
+ **Mathematics for Machine Learning** (M.P. Deisenroth. 2020. Cambridge University Press) [**Web**](https://mml-book.github.io/) | [**Book update**](https://mml-book.github.io/book/mml-book.pdf). [**Book printed**](https://mml-book.github.io/book/mml-book_printed.pdf)
**Deep Learning Concepts**
+ **Principles of Artificial Neural Networks** (Daniel Graupe, 2013): [**Book**](https://www.worldscientific.com/worldscibooks/10.1142/8868)
+ **Principles of Neurocomputing for Science and Engineering** (Fredric M. Ham, 2001): [**Book**](https://www.amazon.com/Principles-Neurocomputing-Science-Engineering-Fredric/dp/0070259666)
+ **Neural Networks and Deep Learning** (M. Nielsen, 2018): [**Book**](http://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf) | [**GitHub**](https://github.com/mnielsen/neural-networks-and-deep-learning) | [**Web**](http://neuralnetworksanddeeplearning.com/)
+ :star: **Neural Networks and Deep Learning** (C.C. Aggarwal, 2018. Springer): [**Book**](https://www.springer.com/gp/book/9783319944623) | [**Web**](http://www.charuaggarwal.net/neural.htm) | [**Slide**](http://www.charuaggarwal.net/AllSlides.pdf)
+ :star: **Deep Learning** (I. Goodfellow, Y. Bengio, & A. Courville. 2016. The MIT Press): [**Book**](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book) | [**GitHub**](https://github.com/lexfridman/mit-deep-learning) | [**Web**](https://www.deeplearningbook.org/)
+ **Math and Architectures of Deep Learning** (K. Chaudhury . 2020. MEAP): [**Book**](https://www.manning.com/books/math-and-architectures-of-deep-learning)
**Computer Vision Concepts**
+ :star: **Computer Vision: Models, Learning, and Inference** (Simon J.D. Prince 2012. Cambridge University Pres): [**Web**](http://www.computervisionmodels.com/) | [**Book**](http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf) | [**GitHub**](https://github.com/krishnadubba/python_cvmodels) | [**Matlab Code**](www.cs.ucl.ac.uk/external/s.prince/book/CVM.rar)
+ **Computer Vision: Algorithms and Application** (R. Szeliski 2010. Springer): [**Book**](http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf) | [**GitHub**](https://github.com/sntchaitu/computer-vision-Algorithms-implementation) | [**Web**](http://szeliski.org/Book/)
## All with Python
**Basic Python Books**
+ **CheatSheet** > [Comprehensive Python Cheatsheet](https://github.com/gto76/python-cheatsheet)
+ :star: **Python 3 Object-oriented Programming** (D. Phillips. 2015. O'Reilly Media): [**Book**](https://www.oreilly.com/library/view/python-3-object-oriented/9781784398781/) | [**GitHub**](https://github.com/PacktPublishing/Python-3-Object-Oriented-Programming-Third-Edition) | [**Web**](https://dusty.phillips.codes/books/)
+ :star: **Learning Python Design Patterns** (G. Zlobin. 2013. Packt): [**Book**](https://www.packtpub.com/application-development/learning-python-design-patterns) | [**GitHub**](https://github.com/freephys/Learning-Python-Design-Patterns)
+ **Mastering Python Design Patterns** (S. Kasampalis & K. Ayeva. 2018. Packt): [**Book**](https://www.packtpub.com/application-development/mastering-python-design-patterns-second-edition) | [**GitHub**](https://github.com/PacktPublishing/Mastering-Python-Design-Patterns-Second-Edition)
+ :star: **Clean Code in Python** (M. Anaya. 2018. Packt): [**Book**](https://www.packtpub.com/application-development/clean-code-python) | [**GitHub**](https://github.com/PacktPublishing/Clean-Code-in-Python)
+ A collection of **design patterns/idioms in Python** (Sakis Kasampalis. GitHub): [**GitHub**](https://github.com/faif/python-patterns)
**Machine Learning with Python**
+ :star: **Introduction to Machine Learning with Python** (A.C. Muler & S. Guido. 2017. O'Reilly Media): [**Book**](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) | [**GitHub**](https://github.com/amueller/introduction_to_ml_with_python) | [**Web**](https://machinelearningmastery.com/start-here/)
+ **Practical Machine Learning with Python** (D. Sarkar, R. Bali, and T. Sharma. 2018. Apress): [**Book**](https://www.apress.com/us/book/9781484232064) | [**GitHub**](https://github.com/dipanjanS/practical-machine-learning-with-python)
+ **Machine Learning Applications Using Python** (P. Mathur. 2019. Apress): [**Book**](https://www.apress.com/gp/book/9781484237861) | [**GitHub**](https://github.com/Apress/machine-learning-applications-using-python)
**Deep Learning with Python**
+ :star: **Deep Learning with Applications Using Python** (N.K. Manaswi, 2018. Apress): [**Book**](https://www.apress.com/gp/book/9781484235157) | [**GitHub**](https://github.com/Apress/Deep-Learning-Apps-Using-Python)
+ :star: **Dive into Deep Learning** - NumPy/MXNet and PyTorch implementations (Aston Zhang, 2020): [**Book**](https://d2l.ai/) | [**GitHub**](https://github.com/d2l-ai/d2l-en)
+ **Dive into Deep Learning Compiler** (Aston Zhang, 2020): [**Book**](https://tvm.d2l.ai/) | [**GitHub**](https://github.com/d2l-ai/d2l-tvm)
+ :star: **Deep Learning with PyTorch** (Eli Stevens, 2020. MEAP): [**Book**](https://pytorch.org/deep-learning-with-pytorch)
**Computer Vision with Python**
+ :star: **Computer Vision with Python 3** (S. Kapur, 2017. Packt): [**Book**](https://www.packtpub.com/application-development/computer-vision-python-3) | [**GitHub**](https://github.com/PacktPublishing/Computer-Vision-with-Python-3)
+ **Programming Computer Vision with Python: Tools And Algorithms For Analyzing Images** (Jan Erik Solem, 2012. O'Reilly): [**Book**](https://www.amazon.com/Programming-Computer-Vision-Python-algorithms/dp/1449316549)
+ **Modern Computer Vision with PyTorch** (V Kishore Ayyadevara, 2020. Packt): [**Book**](https://www.amazon.com/Modern-Computer-Vision-PyTorch-hands/dp/1839213477) | [**GitHub**](https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch)
## All with C++
**Basic C++ Books**
+ **C++ Core Guidelines** (a collaborative effort led by Bjarne Stroustrup, much like the C++ language itself): [**Web**](http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines) | [**GitHub**](https://github.com/isocpp/CppCoreGuidelines)
+ **CheatSheet** > [C++ Cheatsheet](https://github.com/mortennobel/cpp-cheatsheet) | [A cheatsheet of modern C++ language and library features](https://github.com/AnthonyCalandra/modern-cpp-features) | [awesome-cpp1](https://github.com/fffaraz/awesome-cpp) | [awesome-cpp2](https://github.com/rigtorp/awesome-modern-cpp)
+ **cppreference.com** > [**Website**](https://en.cppreference.com/w/Main_Page)
+ **Matplot++**: A C++ Graphics Library for Data Visualization: [GitHub](https://github.com/alandefreitas/matplotplusplus)
+ **Programming: Principles and Practice Using C++** (B. Stroustrup. 2008. Addison-Wesley Professional): [**Book**](https://www.stroustrup.com/programming.html)
+ **The C++ Programming Language** (B. Stroustrup. 2013. Addison-Wesley Professional): [**Book**](https://www.amazon.com/C-Programming-Language-4th/dp/0321563840)
+ **Modern C++ Tutorial**: C++11/14/17/20 On the Fly (O. Changkun. 2020. ): [**Web**](https://changkun.de/modern-cpp/en-us/00-preface/) | [**Book**](https://changkun.de/modern-cpp/pdf/modern-cpp-tutorial-en-us.pdf) | [**GitHub**](https://github.com/changkun/modern-cpp-tutorial)
**Machine Learning with C++**
+ **Hands-On Machine Learning with C++** (K. Kolodiazhnyi, 2020-05. Packt): [**Book**](https://www.packtpub.com/data/hands-on-machine-learning-with-c) | [**GitHub**](https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-CPP)
**Deep Learning with C++**
+ C++ Implementation of **PyTorch Tutorials** for Everyone: [**GitHub**](https://github.com/prabhuomkar/pytorch-cpp)
+ **LibtorchTutorials**: This is a code repository for pytorch c++ (or libtorch) tutorial. [**GitHub**](https://github.com/AllentDan/LibtorchTutorials)
+ [LibtorchDetection](https://github.com/AllentDan/LibtorchDetection): C++ trainable detection library based on libtorch (or pytorch c++). Yolov4 tiny provided now.
+ [LibtorchSegmentation](https://github.com/AllentDan/LibtorchSegmentation): A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
**Image Processing & Computer Vision with C++**
+ **Learning OpenCV 3**: Computer Vision in C++ with the OpenCV Library: [**Book**](https://www.amazon.com/Learning-OpenCV-Computer-Vision-Library-ebook/dp/B01MRXIYAN/ref=sr_1_5?dchild=1&keywords=opencv&qid=1590686604&sr=8-5) | [**GitHub**](https://github.com/oreillymedia/Learning-OpenCV-3_examples)
+ **The CImg Library** is a small and open-source C++ toolkit for image processing: [**Web**](https://cimg.eu/)
## ML Design Patterns & Clean Code Books
+ **Machine Learning Design Patterns** (V. Lakshmanan, S. Robinson, M. Munn. 2020. O'Reilly): [**Book**](https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783) | [**GitHub**](https://github.com/GoogleCloudPlatform/ml-design-patterns)
+ **Clean Machine Learning Code** (M. Taifi, 2020. Leanpub): [**Book**](https://leanpub.com/cleanmachinelearningcode) | [**Course**](https://www.udemy.com/course/clean-machine-learning-code/)
## ML DevOps Books
+ **Building Machine Learning Pipelines**: Automating Model Life Cycles with TensorFlow (Hannes Hapke, 2020. O'Reilly): [**Book**](https://www.amazon.com/Building-Machine-Learning-Pipelines-Automating-ebook/dp/B08CXDBWTX/ref=pd_sim_24?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B08CXDBWTX&psc=1)
+ **Introducing MLOps: How to Scale Machine Learning in the Enterprise** (Mark Treveil. 2020. O'Reilly): [**Book**](https://www.amazon.com/Introducing-MLOps-Machine-Learning-Enterprise/dp/1492083291/ref=sr_1_fkmr0_1?dchild=1&keywords=MLOps+PyTorch&qid=1618832760&s=books&sr=1-1-fkmr0)
+ **Designing Machine Learning Systems** (C. Huyen, 2022. O'Reilly): [**Book**](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/) | [**GitHub**](https://github.com/chiphuyen/dmls-book)
## Deep Learning Frameworks
+ **Deep Learning with Keras** (S. Pal & A. Gulli, 2017. Packt): [**Book**](https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and [**Code**](https://github.com/PacktPublishing/Deep-Learning-with-Keras)
## TensorFlow Frameworks
+ **Project Templates**
+ :star: **Python**: [Tensorflow-Project-Template - MrGemy95](https://github.com/MrGemy95/Tensorflow-Project-Template) 
+ **C++**: Bla-bla.
+ **Awesome Lists**
+ [Awesome TensorFlow - jtoy](https://github.com/jtoy/awesome-tensorflow) 
+ [Awesome TensorFlow 2 - Amin-Tgz](https://github.com/Amin-Tgz/Awesome-TensorFlow-2) 
+ **TensorFlow Books**: [jtoy/awesome-tensorflow#books](https://github.com/jtoy/awesome-tensorflow#books) | [Amin-Tgz/awesome-tensorflow-2#books](https://github.com/Amin-Tgz/awesome-tensorflow-2#books)
+ **Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow** (Sebastian Raschka, **2017**. Packt): [**Book**]() | [**GitHub**](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939/ref=sr_1_10?dchild=1&keywords=tensorflow&qid=1617426020&s=books&sr=1-10)
+ **Deep Learning with Python** (François Chollet, **2017**. Manning): [**Book**](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_5?crid=F69SC23JLIVB&dchild=1&keywords=pytorch&qid=1617425223&s=books&sprefix=PyTorch%2Caps%2C408&sr=1-5) | [**GitHub**]()
+ **Deep Learning with TensorFlow** (G. Zaccone & Md.R. Karim, **2018**. Packt): [**Book**](https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-tensorflow-second-edition), [**Code**](https://www.packtpub.com/codedownloaderrata), and [**GitHub**](https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow)
+ **Deep Learning with TensorFlow 2 and Keras**: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition (Antonio Gulli, **2019**. Packt): [**Book**](https://www.amazon.com/Deep-Learning-TensorFlow-Keras-Regression-ebook/dp/B082MBMFVF/ref=pd_sim_8?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B082MBMFVF&psc=1) | [**GitHub**]()
+ **Hands-On Computer Vision with TensorFlow 2**: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras (Benjamin Planche, **2019**. Packt): [**Book**](https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing-ebook/dp/B07SMQGX48/ref=pd_sim_6?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B07SMQGX48&psc=1) | [**GitHub**]()
+ **Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow**: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron, **2019**. O'Reilly): [**Book**](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?crid=1GTZP3O9SE8K0&dchild=1&keywords=computer+vision+pytorch&qid=1617445657&s=books&sprefix=Computer+Vision%2Cstripbooks-intl-ship%2C418&sr=1-4) | [**GitHub**]()
+ **Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow** (Anirudh Koul, **2019**. O'Reilly): [**Book**](https://www.amazon.com/Practical-Learning-Cloud-Mobile-Hands/dp/149203486X/ref=sr_1_10?crid=1GTZP3O9SE8K0&dchild=1&keywords=computer+vision+pytorch&qid=1617445657&s=books&sprefix=Computer+Vision%2Cstripbooks-intl-ship%2C418&sr=1-10) | [**GitHub**]()
+ **Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow**: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron, **2019**. O'Reilly). [**Book**](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_2?crid=F69SC23JLIVB&dchild=1&keywords=pytorch&qid=1617425223&s=books&sprefix=PyTorch%2Caps%2C408&sr=1-2) | [**GitHub**]()
+ **TensorFlow Lite Books**: [margaretmz/awesome-tensorflow-lite#books](https://github.com/margaretmz/awesome-tensorflow-lite#books)
+ **TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers** (Pete Warden, **2020-01**. O'Reilly Media): [**Book**](https://www.amazon.com/TinyML-Learning-TensorFlow-Ultra-Low-Power-Microcontrollers/dp/1492052043)
+ **Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter**: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS (Anubhav Singh, Rimjhim Bhadani, **2020**. Packt): [**Book**](https://www.amazon.com/Mobile-Deep-Learning-TensorFlow-Flutter/dp/1789611210)
## PyTorch Frameworks
+ **Project Templates**
+ :star: **Python** [victoresque - pytorch-template](https://github.com/victoresque/pytorch-template)
+ **Python** [moemen95 - Pytorch-Project-Template](https://github.com/moemen95/Pytorch-Project-Template)
+ **Python** [L1aoXingyu - Deep-Learning-Project-Template](https://github.com/L1aoXingyu/Deep-Learning-Project-Template)
+ :star: **C++** [kigster - CMake C++ Project Template](https://github.com/kigster/cmake-project-template)
+ **Awesome Lists**
+ [Awesome Pytorch List - bharathgs](https://github.com/bharathgs/Awesome-pytorch-list)  (Framework)
+ :star: [PyTorch Tutorial - Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)  (**Python** Tutorial)
+ [PyTorch Beginner - liaoxingyu](https://github.com/L1aoXingyu/pytorch-beginner)  (**Python** Tutorial)
+ :star: [C++ Implementation of PyTorch Tutorials for Everyone - prabhuomkar](https://github.com/prabhuomkar/pytorch-cpp)  (**C++** Tutorial)
+ :star: [**Libtorch Tutorials**](https://github.com/AllentDan/LibtorchTutorials): This is a code repository for pytorch c++ (or libtorch) tutorial: [LibtorchDetection](https://github.com/AllentDan/LibtorchDetection) and [LibtorchSegmentation](https://github.com/AllentDan/LibtorchSegmentation).
+ [The Incredible PyTorch - ritchieng](https://github.com/ritchieng/the-incredible-pytorch): a curated list of tutorials, papers, projects, communities and more relating to PyTorch.  (Lists)
+ **PyTorch Books**: [rickiepark/awesome-pytorch#books](https://github.com/rickiepark/awesome-pytorch#books)
+ **Deep Learning with PyTorch** (V. Subramanian, **2018**. Packt): [**Book**](https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-pytorch) and [**GitHub**](https://github.com/PacktPublishing/Deep-Learning-with-PyTorch)
+ **Deep Learning with PyTorch 1.0** (S. Yogesh K, **2019**. Packt): [**Book**](https://www.packtpub.com/data/deep-learning-with-pytorch-1-0-second-edition) and [**Code**](https://www.packtpub.com/codedownloaderrata)
+ **Programming PyTorch for Deep Learning**: Creating and Deploying Deep Learning Applications (Ian Pointer, **2019**. O'Reilly): [**Book**](https://www.amazon.com/Programming-PyTorch-Deep-Learning-Applications-ebook/dp/B07Y6181J5/ref=pd_sim_3?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B07Y6181J5&psc=1)
+ **PyTorch Recipes: A Problem-Solution Approach** (Pradeepta Mishra, **2019**. Apress): [**Book**](https://www.amazon.com/PyTorch-Recipes-Problem-Solution-Pradeepta-Mishra-ebook/dp/B07N71V7YJ/ref=pd_sim_21?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B07N71V7YJ&psc=1)
+ **PyTorch Deep Learning Hands-On**: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily (Sherin Thomas, **2019**. Packt): [**Book**](https://www.amazon.com/Hands-Deep-Learning-PyTorch-Facebooks-ebook/dp/B078TLWD3F/ref=pd_sim_23?pd_rd_w=efJGa&pf_rd_p=dc435707-6f1f-492e-b80d-8408db56abc9&pf_rd_r=BZHZQRVCJ5D4JET19ZK7&pd_rd_r=ae7800cb-ed60-49c7-9869-0107ea055b02&pd_rd_wg=AU93j&pd_rd_i=B078TLWD3F&psc=1)
+ :star: **Deep Learning with PyTorch** (Eli Stevens, **2020**. MEAP): [**Book**](https://www.manning.com/books/deep-learning-with-pytorch#toc) and [**Code**](https://www.manning.com/downloads/1754)
+ **Modern Computer Vision with PyTorch** (V.K. Ayyadevara & Y. Reddy, **2020**. Packt): [**Book**](https://www.packtpub.com/product/modern-computer-vision-with-pytorch/9781839213472) and [**Code**](https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch)
+ **Deep Learning for Coders with Fastai and PyTorch**: AI Applications Without a PhD (J. Howard, **2020**. O'Reilly): [**Book**](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527/ref=sr_1_8?crid=F69SC23JLIVB&dchild=1&keywords=pytorch&qid=1617425223&s=books&sprefix=PyTorch%2Caps%2C408&sr=1-8)
+ **PyTorch Computer Vision Cookbook**: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x (Michael Avendi, **2020**. Packt): [**Book**](https://www.amazon.com/PyTorch-Computer-Vision-Cookbook-computer-ebook/dp/B0862CX2ZL)
+ **MLOps for PyTorch**
+ (AutoML) [**AutoPyTorch**](https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch.
+ (CI/CD for Machine Learning) [**CML**](https://github.com/iterative/cml) - Open-source library for implementing CI/CD in machine learning projects.
+ (Hyperparameter Tuning) [**Talos**](https://github.com/autonomio/talos) - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
+ (Model Interpretability) [**Captum**](https://github.com/pytorch/captum) - Model interpretability and understanding library for PyTorch.
+ (Model Serving) [**TorchServe**](https://github.com/pytorch/serve) - A flexible and easy to use tool for serving PyTorch models.
+ (Optimization Tools) [**Horovod**](https://github.com/horovod/horovod) - Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
## Network Programming
+ :star: **Foundations of Python Network Programming** (Brandon Rhodes. 2014. Apress): [**Book**](https://www.apress.com/gp/book/9781430258544) | [**GitHub**](https://github.com/brandon-rhodes/fopnp) 
+ **C++ Network Programming, Volume I**: Mastering Complexity with ACE and Patterns (Douglas Schmidt. 2001. Addison-Wesley Professional): [**Book**](https://www.amazon.com/Network-Programming-Mastering-Complexity-Patterns/dp/0201604647)
+ **C++ Network Programming, Volume 2**: Systematic Reuse with ACE and Frameworks (Douglas Schmidt. 2002. Addison-Wesley Professional): [**Book**](https://www.amazon.com/Network-Programming-Systematic-Reuse-Frameworks/dp/0201795256/ref=pd_bxgy_2/133-5779415-9596742?_encoding=UTF8&pd_rd_i=0201795256&pd_rd_r=4a8eab54-319f-4695-8fb4-cd0f5c4cdd82&pd_rd_w=2CejQ&pd_rd_wg=F4pA9&pf_rd_p=f325d01c-4658-4593-be83-3e12ca663f0e&pf_rd_r=6NY7V1NSC7GMV9R01TTW&psc=1&refRID=6NY7V1NSC7GMV9R01TTW)
## Courses
**Machine Learning**
+ :star: **Belajar Machine Learning Lengkap Dari Nol Banget sampai Practical** - WiraD.K. Putra (2020): [YouTube](https://www.youtube.com/channel/UCszUEDUxCax7i5YsbAt0Tag) | [GitHub](https://github.com/WiraDKP)
+ **Standford Machine Learning** - *Standford* by Andrew Ng (2008): [YoutTube](https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599)
+ **Caltech Machine Learning** - *Caltech* by Yaser Abu-Mostafa (2012-2014): [Web](http://work.caltech.edu/lectures.html)
+ **Neural networks** - *University De Sherbrooke* by Hugo Larochelle (2013): [YouTube](https://www.youtube.com/watch?v=SGZ6BttHMPw&list=PLEAYkSg4uSQ39wGk8XawnlfFYvjVftvUe) | [Web](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
**Deep Learning**
+ :star: **Deep Learning Drizzle** - Mario (2021): [Website](https://deep-learning-drizzle.github.io/) | [GitHub](https://github.com/kmario23/deep-learning-drizzle) 
+ **Carnegie Mellon University Deep Learning** - *CMU*: [YouTube](https://www.youtube.com/watch?v=LmIjgmijyiI&list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj) | [Web](http://deeplearning.cs.cmu.edu/)
+ **Deeplearning.ai Neural Networks and Deep Learning** - *Deeplearning.ai* by Andrew Ng in YouTube (2010-2014): [YouTube](https://www.youtube.com/watch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0&index=2&t=0s)
+ **Standford Neural Networks and Deep Learning** - *Standford* by Fei-Fei Li: [YouTube: 2017](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
+ **MIT Deep Learning** - *MIT* by Lex Fridman: [GitHub](https://github.com/lexfridman/mit-deep-learning) | [YouTube](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U)
+ **Stanford Deep Learning** - *Stanford* by Andrew Ng: [Homepage](https://www.andrewng.org/) | [Web](https://www.deeplearning.ai/) | [Coursera](https://www.coursera.org/specializations/deep-learning) | [GitHub](https://github.com/Kulbear/deep-learning-coursera)
+ **Deep Neural Networks with PyTorch** - *IBM* by Joseph Santarcangelo: [coursera](https://www.coursera.org/learn/deep-neural-networks-with-pytorch)
+ **Deep Learning with PyTorch** - by sentdex: [YouTube](https://www.youtube.com/watch?v=BzcBsTou0C0&list=PLQVvvaa0QuDdeMyHEYc0gxFpYwHY2Qfdh)
+ **Computer Vision** - *Univ. Central Florida* by Mubarak Shah [YouTube](https://www.youtube.com/watch?v=715uLCHt4jE&list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9)
**TinyML**
- **CS249r: Tiny Machine Learning (TinyML)** - *Harvard* by Vijay Janapa Reddi: [sites.google.com](https://sites.google.com/g.harvard.edu/tinyml/home?authuser=0) | [YouTube](https://www.youtube.com/channel/UCLv1K6OaYHP44hXFd5rNqyA) | [edx](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning)| [GitHub](https://github.com/tinyMLx/colabs)
- **Introduction to Embedded Machine Learning** - *Edge Impulse* by Shawn Hymel: [coursera](https://www.coursera.org/learn/introduction-to-embedded-machine-learning)
- **Embedded and Distributed AI** - *Jonkoping University, Sweden* by Beril Sirmacek: [YouTube](https://www.youtube.com/watch?v=OTXqT00MmPA&list=PLyulI6o7oOtycIT15i_I2_mhuLxnNvPvX)
**MLOps**
+ **Machine Learning Engineering for Production MLOps** - by Andrew Ng (2021): [Coursera](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
## Research Groups
**Universities**
+ [**Standford Univ - Machine Learning Group**](https://stanfordmlgroup.github.io/) ([Prof. Andrew Ng](https://www.linkedin.com/in/andrewyng/))
+ [**Standford Univ - Vision and Learning Lab**](http://vision.stanford.edu/) ([Prof. Fei-Fei Li](https://www.linkedin.com/in/fei-fei-li-4541247/))
+ [**Univ of Montreal - Mila**](https://mila.quebec/en/) ([Prof. Yoshua Bengio](https://mila.quebec/en/yoshua-bengio/))
+ [**New York Univ - CILVR Lab**](https://wp.nyu.edu/cilvr/) ([Prof. Yann LeCun](http://yann.lecun.com/))
+ [**Univ of Toronto - Machine Learning**](http://learning.cs.toronto.edu/) ([Prof. Geoffrey Hinton](http://www.cs.toronto.edu/~hinton/))
+ [**Barkeley Univ - Artificial Intelligence Research (BAIR) Lab**](https://vision.berkeley.edu/) ([Prof. Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/))
+ [**MIT - Deep Learning**](https://deeplearning.mit.edu/) ([Lex Fridman](https://lexfridman.com/))
**Communities**
+ :star: [**Q-engineering**](https://qengineering.eu/): Computer vision, Machine learning, Applied mathematics. [GitHub](https://github.com/Qengineering)
+ [PyTorch-Raspberry-Pi-64-OS](https://github.com/Qengineering/PyTorch-Raspberry-Pi-64-OS)
+ [PyTorch-Jetson-Nano](https://github.com/Qengineering/PyTorch-Jetson-Nano)
+ [TensorFlow-Raspberry-Pi-32-bit](https://github.com/Qengineering/TensorFlow-Raspberry-Pi)
+ [TensorFlow-Raspberry-Pi_64-bit](https://github.com/Qengineering/TensorFlow-Raspberry-Pi_64-bit)
+ [TensorFlow-JetsonNano](https://github.com/Qengineering/TensorFlow-JetsonNano)
+ :star: [**HUAWEI Noah's Ark Lab**](https://github.com/huawei-noah): Working with and contributing to the open source community in data mining, artificial intelligence, and related fields.
+ [CV-Backbones](https://github.com/huawei-noah/CV-Backbones): CV backbones including GhostNet, TinyNet and TNT.
+ [Pretrained-Language-Model](https://github.com/huawei-noah/Pretrained-Language-Model): Pretrained language model and its related optimization techniques.
+ :star: [**MIT HAN Lab**](https://github.com/mit-han-lab): Accelerating Deep Learning Computing. [Website](https://hanlab.mit.edu/)
+ [Tiny Machine Learning](https://github.com/mit-han-lab/tinyml): Our projects are covered by: MIT News, WIRED, Morning Brew, Stacey on IoT, Analytics Insight, Techable. [Web](https://hanlab.mit.edu/).
+ [once-for-all](https://github.com/mit-han-lab/once-for-all): [ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment.
+ [proxylessnas](https://github.com/mit-han-lab/proxylessnas): [ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.
+ :star: [**TinyML - Harvard University**](https://github.com/tinyMLx)
+ [tinyMLx - colabs](https://github.com/tinyMLx/colabs): This repository holds the Google Colabs for the EdX TinyML Specialization.
+ [tinyMLx - courseware](https://github.com/tinyMLx/courseware): In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).
+ [arduino-library](https://github.com/tinyMLx/arduino-library): Harvard_TinyMLx Arduino Library.
+ [**NVIDIA Corporation**](https://github.com/NVIDIA)
+ [TRTorch](https://github.com/NVIDIA/TRTorch): PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT.
+ [apex](https://github.com/NVIDIA/apex): A PyTorch Extension: Tools for easy mixed precision and distributed training in PyTorch.
+ [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples): provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.
+ [libcudacxx](https://github.com/NVIDIA/libcudacxx): The C++ Standard Library for your entire system.
+ [**NVIDIA-AI-IOT**](https://github.com/NVIDIA-AI-IOT)
+ [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt): An easy to use PyTorch to TensorRT converter.
+ [tf_trt_models](https://github.com/NVIDIA-AI-IOT/tf_trt_models): TensorFlow models accelerated with NVIDIA TensorRT.
+ [my-jetson-nano-baseboard](https://github.com/NVIDIA-AI-IOT/my-jetson-nano-baseboard): An open source Jetson Nano baseboard and tools to design your own.
+ :star: [**OpenMMLab**](https://github.com/open-mmlab): [mmcv](https://github.com/open-mmlab/mmcv) - OpenMMLab Computer Vision Foundation.
+ [mmclassification](https://github.com/open-mmlab/mmclassification): OpenMMLab Image Classification Toolbox and Benchmark
+ [mmdetection](https://github.com/open-mmlab/mmdetection): OpenMMLab Detection Toolbox and Benchmark.
+ [mmsegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab Semantic Segmentation Toolbox and Benchmark.
+ [mmtracking](https://github.com/open-mmlab/mmtracking): OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.
+ [mmdetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+ [**Open Neural Network Exchange**](https://github.com/onnx): ONNX is an open ecosystem for interoperable AI models. It's a community project: we welcome your contributions!
+ [onnx](https://github.com/onnx/onnx): Open standard for machine learning interoperability.
+ [onnx-tutorial](https://github.com/onnx/tutorials): Tutorials for creating and using ONNX models.
+ [onnx-models](https://github.com/onnx/models): A collection of pre-trained, state-of-the-art models in the ONNX format.
+ [tensorflow-onnx](https://github.com/onnx/tensorflow-onnx): Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX.
+ [onnx-tensorrt](https://github.com/onnx/onnx-tensorrt): ONNX-TensorRT: TensorRT backend for ONNX.
+ [**Cloud-CV**](https://github.com/Cloud-CV): Building platforms for reproducible AI research.
+ [EvalAI](https://github.com/Cloud-CV/EvalAI): Evaluating state of the art in AI.
+ [Fabrik](https://github.com/Cloud-CV/Fabrik): Collaboratively build, visualize, and design neural nets in browser.
+ [Origami](https://github.com/Cloud-CV/Origami): Origami: Artificial Intelligence as a Service.
+ [**Iterative**](https://github.com/iterative): Developer Tools for Machine Learning.
+ [dvc](https://github.com/iterative/dvc): Data Version Control | Git for Data & Models | ML Experiments Management.
+ [cml](https://github.com/iterative/cml): Continuous Machine Learning | CI/CD for ML.
+ :star: [**Machine Learning Tooling**](https://github.com/ml-tooling) - Open-source machine learning tooling to boost your productivity
+ [ml-workspace](https://github.com/ml-tooling/ml-workspace): All-in-one web-based IDE specialized for machine learning and data science.
+ [ml-hub](https://github.com/ml-tooling/ml-hub): Multi-user development platform for machine learning teams. Simple to setup within minutes.
+ :star: [best-of-ml-python](https://github.com/ml-tooling/best-of-ml-python): A ranked list of awesome machine learning Python libraries.
+ :star: [best-of-web-python](https://github.com/ml-tooling/best-of-web-python): A ranked list of awesome python libraries for web development.
+ [opyrator](https://github.com/ml-tooling/opyrator): Turns your machine learning code into microservices with web API, interactive GUI, and more.
+ [**Megvii**](https://github.com/Megvii-BaseDetection) - BaseDetection.
+ [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX): is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported.
+ [cvpods](https://github.com/Megvii-BaseDetection/cvpods): All-in-one Toolbox for Computer Vision Research.
+ [**AMAI GmbH**](https://github.com/AMAI-GmbH): [AI-Expert-Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap): Roadmap to becoming an Artificial Intelligence Expert in 2021.
+ [**Machine Learning Tokyo**](https://github.com/Machine-Learning-Tokyo): [AI_Curriculum](https://github.com/Machine-Learning-Tokyo/AI_Curriculum): Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford, MIT, UC Berkeley.
+ [**Distributed (Deep) Machine Learning Community**](https://github.com/dmlc): [xgboost](https://github.com/dmlc/xgboost)
+ :star: [**EthicalML**: The Institute for Ethical Machine Learning](https://github.com/EthicalML) - The Institute for Ethical Machine Learning is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML.
+ [awesome-production-machine-learning](https://github.com/EthicalML/awesome-production-machine-learning): A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning.
+ [awesome-artificial-intelligence-guidelines](https://github.com/EthicalML/awesome-artificial-intelligence-guidelines): This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
+ :star: [**Hugging Face**](https://github.com/huggingface): The AI community building the future. [Website](https://huggingface.co/)
+ [accelerate](https://github.com/huggingface/accelerate): A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
+ [knockknock](https://github.com/huggingface/knockknock): Knock Knock: Get notified when your training ends with only two additional lines of code.
+ [datasets](https://github.com/huggingface/datasets): The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools.
+ [transformers](https://github.com/huggingface/transformers): Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
**Corporations**
+ :star: [**Tesla AI**](https://www.tesla.com/AI): [**GitHub** Tesla AI](https://github.com/teslamotors)
+ [**Brain Team - Google AI**](https://ai.google/research/teams/brain/): [TensorFlow](https://www.tensorflow.org/), [**GitHub** Google AI Research](https://github.com/google-research)
+ [**Facebook AI**](https://ai.facebook.com/): [PyTorch](https://pytorch.org/), [**GitHub** Facebook Research](https://github.com/facebookresearch)
+ [**Microsoft AI**](https://aischool.microsoft.com/en-us/home): [Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/), [**GitHub** Microsoft AI](https://github.com/microsoft/ailab)
+ [**Amazon AI**](https://aws.amazon.com/ai/): [Alexa](https://github.com/alexa)
+ [**Apple AI**](https://machinelearning.apple.com/)
+ [**Alibaba AI**](https://damo.alibaba.com/labs/ai): [**GitHub** Alibaba AI](https://github.com/alibaba)
+ [**IBM AI**](https://www.research.ibm.com/artificial-intelligence/)
+ [**Nvidia AI**](https://www.nvidia.com/en-us/deep-learning-ai/): [**GitHub** Nvidia AI](https://github.com/NVIDIA-AI-IOT)
+ [**Tencent AI**](https://opensource.tencent.com/): [**GitHub** Tencent AI](https://github.com/Tencent)
+ [**Open AI**](https://openai.com/): [**GitHub** Open AI](https://github.com/openai/)
**Ph.D. in Machine Learning**
+ [**Machine Learning** - Carnegie Mellon University](https://www.ml.cmu.edu/academics/ml-phd.html)
+ [**EECS** - University of California — Berkeley](https://eecs.berkeley.edu/academics/graduate)
+ [**Computer Science** - Stanford University](https://cs.stanford.edu/academics/phd)
+ [**EECS** - Massachusetts Institute of Technology](https://www.eecs.mit.edu/academics-admissions/graduate-program)
+ [**Computer Science** - Cornell University](https://www.engineering.cornell.edu/admissions/graduate-admissions/admissions-phd-students)
**Products**
+ **Self-driving Car**: [Tesla](https://www.tesla.com/en_au) | [Waymo](https://waymo.com/) | [Lyft](https://self-driving.lyft.com/) | [Argo](https://www.argo.ai/) | [Voyage](https://voyage.auto/) | [Aurora](https://aurora.tech/) | [cruise](https://www.getcruise.com/) | []()
+ **Industrial Autonomy & Robotics**: [bostondynamics](https://www.bostondynamics.com/) | [Anki](https://anki.com/) | [Mov.ai](https://mov.ai/)
+ **AI**: [Ultralytics LLC](https://github.com/ultralytics) | [FirmAI](https://github.com/firmai) | [deepdetect.com](https://www.deepdetect.com/)
**AI Start-Up in Indonesia**
+ Institutions: [**ai-innovation.id**](https://ai-innovation.id/) | [**Strategi Nasional Kecerdasan Artifisial (KA)**](https://ai-innovation.id/strategi)
+ ChatBot: [**kata.ai**](https://kata.ai/) > [github.com/kata-ai](https://github.com/kata-ai) & [medium.com/kata-engineering](https://medium.com/kata-engineering) | [**prosa.ai**](https://prosa.ai/) > [medium.com/@prosa.ai](https://medium.com/@prosa.ai) | [**bahasa.ai**](https://www.bahasa.ai/) > [github.com/bahasa-ai](https://github.com/bahasa-ai) & [medium.com/bahasa-ai](https://medium.com/bahasa-ai) | [**aichat.id**](https://aichat.id/) | [**konvergen.ai**](https://konvergen.ai/) > [github.com/konvergen](https://github.com/konvergen) & [medium.com/konvergen](https://medium.com/konvergen)
+ Vision: [**nodeflux.io**](https://www.nodeflux.io/) > [github.com/nodefluxio](https://github.com/nodefluxio) & [medium.com/nodeflux](https://medium.com/nodeflux) & [**retailmatix - nodeflux.io**](https://retailmatix.com/) | [**delligence.ai**](https://delligence.ai/index.html) | [**grit.id**](https://grit.id/index.html) > [github.com/grit-id](https://github.com/grit-id) | [**riset.ai**](https://www.riset.ai/) | [**jagooo.id**](https://jagooo.id/)
+ Data Analytics: [**eureka.ai**](https://eureka.ai/) | [**kepingai.com**](https://kepingai.com/)
+ Annotation Service: [**acquaire - nodeflux.io**](https://acquaire.ai/)
+ Communities: [**Indonesia AI Society**](https://indonesiaai.org/) | [**atapdata.ai**](https://atapdata.ai/) | [**coleaves.ai**](https://coleaves.ai/) | [**jakartamachinelearning**](https://jakartamachinelearning.com/) | [**datascienceID**](https://datascience.or.id/) | [**tau-dataID**](https://tau-data.id/) | [**aidi.id**](http://aidi.id/) | [**idbigdata**](https://idbigdata.com/official/)
## Datasets
[cvpapers.com](http://www.cvpapers.com/datasets.html) | [wikipedia.org](https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research) | [datasetlist.com](https://www.datasetlist.com/) | [deeplearning.net](http://deeplearning.net/datasets/) | [datahub.io](https://datahub.io/) | [towardsai.net](https://towardsai.net/p/machine-learning/best-free-datasets-for-machine-learning-and-data-science/stanfordai/3451/) | [medium-towards-artificial-intelligence](https://medium.com/towards-artificial-intelligence/50-object-detection-datasets-from-different-industry-domains-1a53342ae13d)
+ **MNIST Dataset** - New York University by Yann LeCun (1998): [Raw](http://yann.lecun.com/exdb/mnist/)
+ **Open Images dataset** - [Web](https://storage.googleapis.com/openimages/web/index.html)
+ **YouTube**: [YouTube-BoundingBoxes Dataset](https://research.google.com/youtube-bb/) - E. Real, et. al. | [YouTube-8M Dataset](https://research.google.com/youtube8m/) - S. Abu-El-Haija, et. al. (2017) | [YouTube-VOS Dataset](https://youtube-vos.org/dataset/) - Ning Xu, et. al. (2018)
+ **H3D Dataset** - Honda by Abhishek Patil et. al. (2019): [Paper](https://usa.honda-ri.com/documents/32932/126732/h3d_paper.pdf/6d92fba0-06f8-6a12-5d71-b6feb9ba0842) | [Web](https://usa.honda-ri.com/H3D)
+ **BLVD Dataset** - Xian Jiaotong University by Jianru Xue, et. al. (2019): [Paper](https://arxiv.org/pdf/1903.06405.pdf) | [GitHub](https://github.com/VCCIV/BLVD)
+ **Awesome Vehicle Dataset**: [**manfreddiaz**](https://github.com/manfreddiaz/awesome-autonomous-vehicles#datasets) | [**hunjung-lim**](https://github.com/hunjung-lim/awesome-vehicle-datasets) | [**AmiTitus**](https://github.com/AmiTitus/awesome-vehicle-datasets)
**Vehicle Classification**
+ **Vehicle image database** - Universidad Politécnica de Madrid (UPM) by J. Arróspide (2012) - **3425 images** of vehicle rears: [Web](http://www.gti.ssr.upm.es/data/Vehicle_database.html)
**Object Detection & Recognition**
+ **CIFAR10 [10]** - University of Toronto by Alex Krizhevsky (2009): [Raw](https://www.cs.toronto.edu/~kriz/cifar.html) (**10 classes**: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck) | [pdf](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
+ **PASCAL VOC [20]** - M. Everingham (2012): [Raw](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html) (**20 classes**: **person**: person; **animal**:bird, cat, cow, dog, horse, sheep; **vehicle**: aeroplane, bicycle, boat, bus, car, motorbike, train; **indoor**: bottle, chair, dining table, potted plant, sofa, tv/monitor) | [pdf](http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf)
+ **COCO [80]** - COCO Consortium by Tsung-Yi Lin, et. al. (2015): [Web](http://cocodataset.org/) | [Download](http://cocodataset.org/#download) (**80 classes**: person & accessory, animal, vehicle, aoutdoor objects, sports, kitchenware, food, furniture, appliance, electronics, and indoor objects) | [pdf](https://arxiv.org/abs/1405.0312)
+ **CIFAR100 [100]** - University of Toronto by Alex Krizhevsky (2009): [Raw](https://www.cs.toronto.edu/~kriz/cifar.html) (**100 classes**: **aquatic mammals**: beaver, dolphin, otter, seal, whale; **fish**: aquarium fish, flatfish, ray, shark, trout, **flowers**: orchids, poppies, roses, sunflowers, tulips; **food containers**: bottles, bowls, cans, cups, plates; **fruit and vegetables**: apples, mushrooms, oranges, pears, sweet peppers; **household electrical devices**: clock, computer keyboard, lamp, telephone, television; **household furniture**: bed, chair, couch, table, wardrobe; **insects**: bee, beetle, butterfly, caterpillar, cockroach; **large carnivores**: bear, leopard, lion, tiger, wolf; **large man-made outdoor things**: bridge, castle, house, road, skyscraper; **large natural outdoor scenes**: cloud, forest, mountain, plain, sea; **large omnivores and herbivores**: camel, cattle, chimpanzee, elephant, kangaroo; **medium-sized mammals**: fox, porcupine, possum, raccoon, skunk; **non-insect invertebrates**: crab, lobster, snail, spider, worm; **people**: baby, boy, girl, man, woman; **reptiles**: crocodile, dinosaur, lizard, snake, turtle; **small mammals**: hamster, mouse, rabbit, shrew, squirrel; **trees**: maple, oak, palm, pine, willow; **vehicles 1**: bicycle, bus, motorcycle, pickup truck, train; **vehicles 2**: lawn-mower, rocket, streetcar, tank, tractor) | [pdf](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
+ **ImageNet [10,000]** Stanford University by Olga Russakovsky (2012) - [Raw](http://www.image-net.org/challenges/LSVRC/2012/index) | [pdf](https://link.springer.com/article/10.1007/s11263-015-0816-y?sa_campaign=email/event/articleAuthor/onlineFirst)
**Object Tracking**
+ **KITTI [2]**: [Raw](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)(**2 classes**: car & pedestrian) | [pdf](http://www.cvlibs.net/publications/Geiger2012CVPR.pdf)
+ **LaSOT [85]**: A High-quality Large-scale Single Object TrackingBenchmark - Stony Brook University by Heng Fan (2020): [Raw](http://vision.cs.stonybrook.edu/~lasot/) (**85 classes**) | [pdf](https://arxiv.org/pdf/2009.03465.pdf)
+ **MOT16**: A Benchmark for Multi-Object Tracking - Univ. of Adelaide by A. Milan, et. al. (2016)]: [Raw](https://motchallenge.net/data/MOT16/) | [pdf](https://arxiv.org/abs/1603.00831)
+ **TAO [833]**: A Large-Scale Benchmark for Tracking Any Object - Carnegie Mellon University by Achal Dave (2020): [Raw](https://taodataset.org/) (**833 classes**) | [pdf](https://arxiv.org/abs/2005.10356)
**Monocular 3D Object Detection**
+ **KITTI Dataset** - University of Tübingen by Andreas Geiger (**2012**): [Raw](http://www.cvlibs.net/datasets/kitti/raw_data.php) | [Object 2D](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d) | [Object 3D](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) | [Bird's Eye View](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=bev) (**8 classes**: car, van, truck, pedestrian, person_sitting, cyclist, tram, and misc or don’t care)
+ **Boxy Dataset** - bosch-ai by Karsten Behrendt (**2019**): [Web](https://boxy-dataset.com/boxy/) | [2D Box](https://boxy-dataset.com/boxy/benchmark_2d) | [3D Box](https://boxy-dataset.com/boxy/benchmark_3d) | [Realtime](https://boxy-dataset.com/boxy/benchmark_realtime) | [Paper](https://ieeexplore.ieee.org/document/9022257) (**1 classes**: freeways {passenger cars, trucks, campers, boats, car carriers, construction equipment, and motorcycles}, heavy traffic, traffic jams)
+ **nuScenes** - nuTonomy by Holger Caesar (**2019-03**) The nuScenes dataset is a large-scale autonomous driving dataset: [Link](https://www.nuscenes.org/) | [Toolbox](https://github.com/nutonomy/nuscenes-devkit) | [Paper](https://arxiv.org/abs/1903.11027) (**23 classes | 19 detection**: animal, debris, pushable, bicycle, ambulance, police, barrier, bicycle, bus, car, construction vehicle, motorcycle, pedestrian, personal mobility, stroller, wheelchair, traffic cone, trailer, truck)
+ **Cityscapes3D** - Mercedes-Benz AG by Nils Gählert (**2020-06**), Dataset and Benchmark for Monocular 3D Object Detection: [Link](https://www.cityscapes-dataset.com) | [Toolbox](https://github.com/mcordts/cityscapesScripts) | [Paper](https://arxiv.org/abs/2006.07864) (**8 classes**: car, truck, bus, on rails, motorcycle, bicycle, caravan, and trailer)
## Hardware
[edge-ai - crespum](https://github.com/crespum/edge-ai#hardware)
**Edge Hardware**
+ :star: **Jetson Nano Dev Board** - brings accelerated AI performance to the Edge in a power-efficient and compact form factor: [**Website**](https://developer.nvidia.com/embedded/jetson-modules) | [**GitHub**](https://github.com/dusty-nv/jetson-inference)
+ :star: **Google Coral Dev Board** - is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, fast and offline: [**Website**](https://coral.ai/docs/dev-board/get-started) | [**GitHub**](https://github.com/google-coral)
+ **Intel Movidius Neural Compute Sticks** - enables rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge. : [**Website**](https://ark.intel.com/content/www/us/en/ark/products/125743/intel-movidius-neural-compute-stick.html) | [**GitHub**](https://github.com/movidius)
+ **ARM microNPU** - Processors designed to accelerate ML inference (being the first one the Ethos-U55): [**Website**](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55)
+ **Espressif ESP32-S3** - SoC similar to the well-known ESP32 with support for AI acceleration (among many other interesting differences): [**Website**](https://www.espressif.com/en/products/socs/esp32-s3)
+ :star: **RaspberryPi/Arduino/STM32 + Edge Impulse** - Enabling developers to create the next generation of intelligent device solutions through embedded Machine Learning: [**Website**](https://www.edgeimpulse.com/) | [**GitHub**](https://github.com/edgeimpulse)
+ [**OpenMV**](http://docs.openmv.io) - A camera that runs with MicroPython on ARM Cortex M6/M7 and great support for computer vision algorithms. Now with [support for Tensorflow Lite too](https://openmv.io/blogs/news/tensorflow-lite-and-person-detection).
+ [**JeVois**](http://jevois.org/) - A TensorFlow-enabled camera module.
+ [**Maxim MAX78000**](https://www.maximintegrated.com/en/products/microcontrollers/MAX78000.html) - SoC based on a Cortex-M4 that includes a CNN accelerator.
+ [**Beagleboard BeagleV**](https://beagleboard.org/beaglev) - Open Source RISC-V-based Linux board that includes a Neural Network Engine.
**Processor**: [The Deep Learning Compiler: A Comprehensive Survey - arXiv '20](https://arxiv.org/abs/2002.03794)
+ Tensor Processing Unit (**TPU**) by Google: [Wiki](https://en.wikipedia.org/wiki/Tensor_Processing_Unit)
+ Neural Processing Unit (**NPU**) by MobilePhone Company: [Wiki](https://en.wikichip.org/wiki/neural_processor)
+ Vision Processing Unit (**VPU**) by NEC & Intel: [Wiki](https://en.wikipedia.org/wiki/Vision_processing_unit)
+ Intelligence Processing Unit (**IPU**) by Graphcore: [GitHub](https://github.com/graphcore)
+ Machine Learning Unit (**MLU**) by Cambricon: [GitHub](https://github.com/Cambricon)
## Deep Learning for Embedded (IOT) & Mobile Devices
**Frameworks**
[Embedded and mobile deep learning - csarron](https://github.com/csarron/awesome-emdl) | [Awesome Mobile Machine Learning - fritzlabs](https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning) | [Awesome Edge Machine Learning - Bisonai](https://github.com/Bisonai/awesome-edge-machine-learning) | [edge-ai - crespum](https://github.com/crespum/edge-ai#software) | [AI-performance - embedded-ai.bench](https://github.com/AI-performance/embedded-ai.bench)
+ :star: [TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite): **TensorFlow Lite** is an open source deep learning framework for on-device inference.
+ [The Arm's ComputeLibrary framework](https://github.com/ARM-software/ComputeLibrary): **ComputeLibrary** is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
+ [The Alibaba's MNN framework](https://github.com/alibaba/MNN): **MNN** is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.
+ :star: [The Tencent's ncnn framework](https://github.com/Tencent/ncnn): **ncnn** is a high-performance neural network inference framework optimized for the mobile platform.
+ [The Baidu's Paddle Lite framework](https://github.com/PaddlePaddle/Paddle-Lite): **Paddle Lite** is multi-platform high performance deep learning inference engine.
+ [The XiaoMi's Mace framework](https://github.com/XiaoMi/mace): **MACE** is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
+ [The Apple's CoreML framework](https://developer.apple.com/documentation/coreml): **CoreML** is integrate machine learning models into your app.
+ [The Microsoft's ELL framework](https://github.com/Microsoft/ELL): **ELL** allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit.
+ :star: [PyTorch Mobile](https://pytorch.org/mobile/home/): **PyTorch Mobile** is a new framework for helping mobile developers and machine learning engineers embed PyTorch ML models on-device.
+ [dabnn - JDAI Computer Vision](https://github.com/JDAI-CV/dabnn): **dabnn** is an accelerated binary neural networks inference framework for mobile platform.
+ [opencv-mobile](https://opencv.org/platforms/): **opencv-mobile** is open source computer vision library that was designed to be cross-platform. The minimal opencv for Android, iOS and ARM Linux.
+ [DeepLearningKit](https://github.com/DeepLearningKit/DeepLearningKit): **DeepLearningKit** is Open Source Deep Learning Framework for Apple's iOS, OS X and tvOS.
+ [Tengine - OAID](https://github.com/OAID/Tengine): **Tengine** is a lite, high performance, modular inference engine for embedded device.
+ [Bender](https://github.com/xmartlabs/Bender): **Bender** is easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
+ [uTensor](https://github.com/uTensor/uTensor) - AI inference library based on mbed (an RTOS for ARM chipsets) and TensorFlow.
+ [CMSIS NN](https://arm-software.github.io/CMSIS_5/NN/html/index.html) - A collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
+ [ARM Compute Library](https://developer.arm.com/technologies/compute-library) - Set of optimized functions for image processing, computer vision, and machine learning.
+ [Qualcomm Neural Processing SDK for AI](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk) - Libraries to developers run NN models on Snapdragon mobile platforms taking advantage of the CPU, GPU and/or DSP.
+ [X-CUBE-AI](https://blog.st.com/stm32cubeai-neural-networks/) - Toolkit for generating NN optimiezed for STM32 MCUs.
+ [Neural Network on Microcontroller (NNoM)](https://github.com/majianjia/nnom) - Higher-level layer-based Neural Network library specifically for microcontrollers. Support for CMSIS-NN.
+ [nncase](https://github.com/kendryte/nncase) - Open deep learning compiler stack for Kendryte K210 AI accelerator.
+ [deepC](https://github.com/ai-techsystems/dnnCompiler) - Deep learning compiler and inference framework targeted to embedded platform.
+ [uTVM](https://tvm.apache.org/2020/06/04/tinyml-how-tvm-is-taming-tiny) - *MicroTVM* is an open source tool to optimize tensor programs.
+ [Edge Impulse](https://edgeimpulse.com/) - Interactive platform to generate models that can run in microcontrollers. They are also quite active on social netwoks talking about recent news on EdgeAI/TinyML.
**Books**
1. [Mobile Edge Artificial Intelligence](https://www.elsevier.com/books/mobile-edge-artificial-intelligence/shi/978-0-12-823817-2) [Elsevier '21]
## Tools
**Production**
+ [docker.com](https://www.docker.com/): build and ship apps.
+ [onnx.ai](https://onnx.ai/): open format built to represent machine learning models.
+ [mlflow.org](https://mlflow.org/): an open source platform for the machine learning lifecycle.
+ [cortex.dev](https://www.cortex.dev/): the open source stack for machine learning engineering.
+ [mlperf.org](https://mlperf.org/): Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.
+ [grpc](https://github.com/grpc/grpc): A high performance, open source, general-purpose RPC framework.
+ [gpustat](https://github.com/wookayin/gpustat): A simple command-line utility for querying and monitoring GPU status.
+ [jetson-stats](https://github.com/rbonghi/jetson_stats): Simple package for monitoring and control your NVIDIA Jetson [Xavier NX, Nano, AGX Xavier, TX1, TX2].
+ [nnabla-ext-cuda](https://github.com/sony/nnabla-ext-cuda): A CUDA Extension of Neural Network Libraries.
**Training Model**
+ [DIGITS](https://github.com/NVIDIA/DIGITS): DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow.
+ [Optuna](https://github.com/optuna/optuna): A hyperparameter optimization framework.
+ [Determined](https://github.com/determined-ai/determined): Deep Learning Training Platform.
+ [cuDF](https://github.com/rapidsai/cudf): GPU DataFrame Library.
+ [DeepSpeed](https://github.com/microsoft/DeepSpeed): DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
+ [comet.ml](https://www.comet.ml/site/): track, compare, explain and optimize experiments and models.
+ [dvc](https://github.com/iterative/dvc): Data Version Control | Git for Data & Models.
+ [Weights & Biases](https://wandb.ai/site): Experiment tracking, model and dataset versioning, hyperparameter optimization.
+ [modelzoo.co](https://modelzoo.co/): Discover open source deep learning code and pretrained models.
**Visualization: Architecture**
+ :star: [**Netron**](https://github.com/lutzroeder/netron): a viewer for neural network, deep learning and machine learning models.
+ :star: [**NN-SVG**](http://alexlenail.me/NN-SVG/LeNet.html): Publication-ready NN-architecture schematics.
+ :star: [**ennui**](https://math.mit.edu/ennui/): Working on a drag-and-drop neural network visualizer (and more). Here's an example of a visualization for a LeNet-like architecture.
+ [**TensorSpace**](https://tensorspace.org/index.html): TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc.
+ [**netscope**](http://dgschwend.github.io/netscope/quickstart.html): A web-based tool for visualizing and analyzing convolutional neural network architectures (or technically, any directed acyclic graph).
+ [**playground**](https://github.com/tensorflow/playground): Deep playground is an interactive visualization of neural networks, written in TypeScript using d3.js.
+ [PerceptiLabs](https://github.com/PerceptiLabs/PerceptiLabs): a dataflow driven, visual API for TensorFlow that enables data scientists to work more efficiently with machine learning models and to gain more insight into their models.
+ [conv](https://www.cs.ryerson.ca/~aharley/vis/conv/): 3D visualization of convolutional neural network.
+ [PyTorchViz](https://github.com/szagoruyko/pytorchviz): A small package to create visualizations of PyTorch execution graphs and traces.
+ [PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet): Latex code for making neural networks diagrams.
+ [ml-visuals](https://github.com/dair-ai/ml-visuals): ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
+ [traingenerator](https://github.com/jrieke/traingenerator): A web app to generate template code for machine learning.
+ [nni](https://github.com/microsoft/nni): an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
+ [nn-visualizer](https://github.com/stefsietz/nn-visualizer): Interactive 3D Neural Network Visualizer.
**Dashboard**
+ [**wave**](https://github.com/h2oai/wave) - Realtime Web Apps and Dashboards for Python and R.
+ [**mediapipe**](https://github.com/google/mediapipe) - Cross-platform, customizable ML solutions for live and streaming media.
+ [**Flask JSONDash**](https://github.com/christabor/flask_jsondash) - Build complex dashboards without any front-end code.
+ [**thingsboard**](https://github.com/thingsboard/thingsboard) - Open-source IoT Platform - Device management, data collection, processing and visualization.
+ [**freeboard**](https://github.com/Freeboard/freeboard) - A damn-sexy, open source real-time dashboard builder for IOT and other web mashups.
+ Augmented Reality (VR) & Virtual Reality (VR) Dashboard
+ [**ViroReact**](https://github.com/viromedia/viro): AR and VR using React Native.
+ Awesome lists: [Domeee/awesome-augmented-reality](https://github.com/Domeee/awesome-augmented-reality), [dharmeshkakadia/awesome-AR](https://github.com/dharmeshkakadia/awesome-AR), [Vytek/VR-Awesome](https://github.com/Vytek/VR-Awesome), [mnrmja007/awesome-virtual-reality](https://github.com/mnrmja007/awesome-virtual-reality)
## Interested Research
+ **Deep Learning Models** - A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks: [**GitHub**](https://github.com/rasbt/deeplearning-models) 
+ **Hyperparameter Optimization of Machine Learning Algorithms** - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear): [**GitHub**](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms) 
+ **FairMOT** - A simple baseline for one-shot multi-object tracking: [**GitHub**](https://github.com/ifzhang/FairMOT) 
+ **Norfair** - is a customizable lightweight Python library for real-time 2D object tracking: [**GitHub**](https://github.com/tryolabs/norfair)
+ **Transformer**: [Awesome Visual-Transformer](https://github.com/dk-liang/Awesome-Visual-Transformer) | [pytorch2libtorch](https://github.com/dk-liang/pytorch2libtorch) | [Fast Transformers](https://github.com/idiap/fast-transformers)
**Autonomous Vehicles**
+ **Awesome Autonomous Vehicles** - manfreddiaz: [**GitHub**](https://github.com/manfreddiaz/awesome-autonomous-vehicles) 
+ **Autoware** - Integrated open-source software for urban autonomous driving: [**Web**](www.autoware.ai/) | [**GitHub**](https://github.com/Autoware-AI/autoware.ai) 
+ **CARLA Simulator** - Open-source simulator for autonomous driving research: [**GitHub**](https://github.com/carla-simulator/carla) 
+ Self-DrivingToy Car - experiencor: [**GitHub**](https://github.com/experiencor/self-driving-toy-car)
+ [openpilot](https://github.com/commaai/openpilot): is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 85 supported car makes and models.
## Benchmark
[benchmarks.ai](https://benchmarks.ai) | [dawn.cs.stanford.edu](https://dawn.cs.stanford.edu/benchmark/) | [mlperf.org](https://mlperf.org/) | [MobilePhone - ai-benchmark.com](http://ai-benchmark.com/index.html) | GitHub > [deep-learning-benchmark - u39kun](https://github.com/u39kun/deep-learning-benchmark), [DeepBench - baidu-research](https://github.com/baidu-research/DeepBench)
+ [**MLPerf**](https://mlcommons.org/en/) is a trademark of MLCommon
+ [Training High Performance Computing](https://mlcommons.org/en/training-hpc-07/)
+ [Training normal](https://mlcommons.org/en/training-normal-07/)
+ [Data Center](https://mlcommons.org/en/inference-datacenter-10/)
+ [Edge](https://mlcommons.org/en/inference-edge-10/)
+ [Tiny](https://mlcommons.org/en/inference-tiny/)
+ [Mobile](https://mlcommons.org/en/inference-mobile-07/)
+ **GPUs Benchmark** [videocardbenchmark.net](https://www.videocardbenchmark.net/) | [gpu.userbenchmark.com](https://gpu.userbenchmark.com/)
+ [Deep Learning GPU Benchmarks](https://lambdalabs.com/gpu-benchmarks)
+ [Video Card Benchmark](https://www.videocardbenchmark.net/directCompute.html)
+ [CPU Benchmark](https://www.cpubenchmark.net/high_end_cpus.html)
+ Tools
+ [Jetson Nano: Deep Learning Inference Benchmarks](https://developer.nvidia.com/embedded/jetson-nano-dl-inference-benchmarks)
+ [2017-02 - Benchmarking State-of-the-Art Deep Learning Software Tools](https://arxiv.org/pdf/1608.07249.pdf)
+ [2019-07 - Benchmarking TPU, GPU, and CPU Platforms for Deep Learning](https://arxiv.org/pdf/1907.10701.pdf)
+ [2019-04-20 - Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board — Hardware Comparison](https://towardsdatascience.com/google-coral-edge-tpu-board-vs-nvidia-jetson-nano-dev-board-hardware-comparison-31660a8bda88)
+ Object Classification
+ [MNIST](https://benchmarks.ai/mnist)
+ [CIFAR-10](https://benchmarks.ai/cifar-10)
+ [CIFAR-100](https://benchmarks.ai/cifar-100)
+ Object Detection
+ [PASCAL VOC](https://benchmarks.ai/voc-2012-comp4)
+ [COCO](https://benchmarks.ai/coco-detection)
+ [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d)
+ Multi-Object Tracking
+ [MOT16](https://motchallenge.net/results/MOT16/)
+ [MOT20](https://motchallenge.net/results/MOT20/)
+ [KITTI](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)
+ NLP
+ [IndoNLU Benchmark](https://www.indobenchmark.com/)
## Create Datasets
+ **Get Image from Sources**
+ [Creating a dataset using an API with Python](https://towardsdatascience.com/creating-a-dataset-using-an-api-with-python-dcc1607616d)
+ [How to create a dataset in Google Colab for your Machine Learning projects](https://medium.com/@gianfrancescoangelini/how-to-create-a-dataset-in-google-colab-for-your-machine-learning-projects-c1852d62936e)
+ [How to (quickly) build a deep learning image dataset](https://www.pyimagesearch.com/2018/04/09/how-to-quickly-build-a-deep-learning-image-dataset/)
+ [Tips for building large image datasets](https://forums.fast.ai/t/tips-for-building-large-image-datasets/26688)
+ **Dataset Tools**
+ [Dataset Search - google](https://toolbox.google.com/datasetsearch)
+ [awesome dataset tools - a curated list of awesome dataset tools](https://github.com/jsbroks/awesome-dataset-tools)
## Journals, Magazines, and People
**Journals**
+ **AI**: [Artificial Intelligence (Q1)](https://www.journals.elsevier.com/artificial-intelligence/) | [Journal of Artificial Intelligence Research (Q1)](https://www.jair.org/index.php/jair) | [Artificial Intelligence Review (Q1)](https://www.springer.com/journal/10462)
+ **Machine Learning**: [Journal of Machine Learning Research (Q1)](http://jmlr.csail.mit.edu/) | [Machine Learning (Q1)](https://www.springer.com/computer/ai/journal/10994) | [Foundations and Trends in Machine Learning (Q1)](https://www.nowpublishers.com/mal)
+ **Computer Vision**: [Image and Vision Computing (Q1)](https://www.journals.elsevier.com/image-and-vision-computing/) | [Computer Vision and Image Understanding (Q1)](https://www.journals.elsevier.com/computer-vision-and-image-understanding) | [International Journal of Computer Vision (Q1)](https://www.springer.com/journal/11263)
**Magazines**: [towardsdatascience](https://towardsdatascience.com) | [paperswithcode](https://paperswithcode.com/) | [distill](https://distill.pub/) | [xenonstack](https://www.xenonstack.com/) | [awesomeopensource.com](https://awesomeopensource.com/) | [emerge-ai.com](https://emerge-ai.com/)
+ **AI**: [towards-artificial-intelligence - AI ](https://medium.com/towards-artificial-intelligence) | [towardsdatascience - AI](https://towardsdatascience.com/artificial-intelligence/home) | [AI - ID](https://artificialintelligence.id/)
+ **Machine Learning**: [towardsdatascience - ML](https://towardsdatascience.com/machine-learning/home) | [ML - ID](https://medium.com/@machinelearningid) | [jakartamachinelearning](https://jakartamachinelearning.com/)
+ **Deep Learning**: [paperswithcode - NLP](https://paperswithcode.com/area/natural-language-processing) | [deeplearningweekly.com](https://www.deeplearningweekly.com/)
+ **Computer Vision**: [paperswithcode - CV](https://paperswithcode.com/area/computer-vision)
**People**
+ **AI**: [Ayu Purwarianti, Dr (Computer Science, Toyohashi University of Technology)](https://www.linkedin.com/in/ayu-purwarianti/) | [Igi Ardiyanto, Dr (Robotics, Toyohashi University of Technology)](https://www.linkedin.com/in/igi-ardiyanto-07516766) | [Muhammad Ghifary, PhD (AI, Victoria University of Wellington)](https://www.linkedin.com/in/muhammad-ghifary-75056722)
+ **Machine Learning**: [Dwi H. Widyantoro, Dr (Machine Learning, Texas A&M University)](https://scholar.google.com/citations?user=lyaV0HgAAAAJ&hl=en)
**Podcast**
+ **Indonesian Tech/Dev**: [Ceritanya Developer Podcast - Riza Fahmi](https://open.spotify.com/show/6grT1c7jDkhK4skm1YIsTs) | [Kode Nol - deep tech foundation](https://open.spotify.com/show/0919qUs3HI9pgoKENxC5VY)
+ **Indonesian StartUp**: [Startup Studio Indonesia - Startup Studio Indonesia](https://open.spotify.com/show/2UFPq0jIelIlLPduFyLEYw) | [The Spectrum Talks - Anggriawan Sugianto](https://open.spotify.com/show/7D2MQnYYeui5DsXzfWMLXF?si=q1AFCiuuQ4CMtUTl_X9Mdg) | [Ngobrolin Startup & Teknologi - Imre Nagi](https://open.spotify.com/show/3cA81ivwFR2gDMF570j06X) | [Startup Hour by StartupIndonesia: StartupIndonesia](https://open.spotify.com/show/0jIz5Q3Bm2y7IO3XsQSBFO) | [#NgobrolinStartup - Dailysocial Podcast](https://open.spotify.com/show/5nn1jczLgAfiyrkdBGk5u2)
+ **Data Science**: [Towards Data Science - The TDS team](https://open.spotify.com/show/63diy2DtpHzQfeNVxAPZgU) | [DataPods - Data Science Indonesia](https://open.spotify.com/show/356i7xRQBUFukuL25UKsf1?si=fODHPgpuSv69xUktjbYQKw) | [Data Talks - KBR Prime x Algoritma](https://open.spotify.com/show/5rDY9Yt7vZE3NGlrypIKPI?si=3_iP2frHQxGvebwaahCsqw)
+ **AI**: [AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion - Cognilytica](https://open.spotify.com/show/4z2M4S9e0K6yk3KB9XrO7h) | [Practical AI: Machine Learning & Data Science](https://open.spotify.com/show/1LaCr5TFAgYPK5qHjP3XDp?si=97a-K3SQSjuna7sK44DlWA) | [Lex Fridman Podcast - Lex Fridman](https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL)
+ **IoT**: [IoT For All Podcast - IoT For All](https://open.spotify.com/show/0jYLPvfCrBZVCwM5a7aldP?si=V3T5ASf4SOiX3kPoeSbPqw) | [IOTALK - IOTIZEN](https://open.spotify.com/show/1Lbma7v1cl1VdgUjGF0gOb?si=4caQHTsORh-cCie_y-OEuQ)
**Conferences & Competitions for Image Processing & Computer Vision**: [guide2research.com](http://www.guide2research.com/topconf/computer-vision) | [openaccess.thecvf.com](https://openaccess.thecvf.com/menu)
+ :star: **CVPR**: IEEE/CVF Conference on Computer Vision and Pattern Recognition. Paper list: https://openaccess.thecvf.com/CVPR2020
+ :star: **LPCV**: Low Power Computer Vision Competition. Website: https://lpcv.ai/
+ **ICCV**: IEEE/CVF International Conference on Computer Vision. Paper list: https://openaccess.thecvf.com/ICCV2019
+ **ECCV**: European Conference on Computer Vision. Paper list: https://link.springer.com/conference/eccv
+ **WACV**: Workshop on Applications of Computer Vision. Paper list: https://openaccess.thecvf.com/WACV2020
+ **3DV**: International Conference on 3D Vision. Website http://3dv2020.dgcv.nii.ac.jp/index.html
+ **ACCV**: Asian Conference on Computer Vision (ACCV). Website: http://accv2020.kyoto/
+ **AAAI**: Association for the Advancement of Artificial Intelligence. Website: https://aaai.org/Conferences/conferences.php