/Machine-Learning-and-Computer-Vision-References

Repository for Machine Learning and Computer Vision references.

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Machine Learning, Deep Learning, and Computer Vision References

Data is the new oil? No: Data is the new soil. ~ David McCandless

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⭐ - Recommendations for Beginners.

Awesome Lists

Artificial Intelligence

Machine Learning

Deep Learning

Computer Vision

Concepts

Mathematics Concepts

  • ProofWiki (proofwiki.org): Web
  • Book of Proof (Richard Hammack, 2018, 3rd Ed.): Book | Web
  • Book of Proofs (bookofproofs.org): Web

Machine Learning Concepts

  • ⭐ Pengenalan Pembelajaran Mesin dan Deep Learning (J.W.G. Putra, 2019): Book | GitHub | Web
  • Machine Learning Probabilistic Prespective (K.P. Murphy, 2012. The MIT Press): Book | GitHubGitHub stars | Solution | Web
  • Pattern Recognition and Machine Learning (C.M. Bishop. 2006. Springer): Book | GitHubGitHub stars | Web
  • Mathematics for Machine Learning (M.P. Deisenroth. 2020. Cambridge University Press) Web | Book update. Book printed

Deep Learning Concepts

  • Principles of Artificial Neural Networks (Daniel Graupe, 2013): Book
  • Principles of Neurocomputing for Science and Engineering (Fredric M. Ham, 2001): Book
  • Neural Networks and Deep Learning (M. Nielsen, 2018): Book | GitHubGitHub stars | Web
  • ⭐ Neural Networks and Deep Learning (C.C. Aggarwal, 2018. Springer): Book | Web | Slide
  • ⭐ Deep Learning (I. Goodfellow, Y. Bengio, & A. Courville. 2016. The MIT Press): Book | GitHubGitHub stars | Web
  • Math and Architectures of Deep Learning (K. Chaudhury . 2020. MEAP): Book

Computer Vision Concepts

  • ⭐ Computer Vision: Models, Learning, and Inference (Simon J.D. Prince 2012. Cambridge University Pres): Web | Book | GitHubGihttps://deeplearning.mit.edu/tHub stars | Matlab Code
  • Computer Vision: Algorithms and Application (R. Szeliski 2010. Springer): Book | GitHubGitHub stars | Web

All with Python

Basic Python Books

  • CheatSheet > Comprehensive Python CheatsheetGitHub stars
  • ⭐ Python 3 Object-oriented Programming (D. Phillips. 2015. O'Reilly Media): Book | GitHubGitHub stars | Web
  • ⭐ Learning Python Design Patterns (G. Zlobin. 2013. Packt): Book | GitHubGitHub stars
  • Mastering Python Design Patterns (S. Kasampalis & K. Ayeva. 2018. Packt): Book | GitHubGitHub stars
  • ⭐ Clean Code in Python (M. Anaya. 2018. Packt): Book | GitHubGitHub stars
  • A collection of design patterns/idioms in Python (Sakis Kasampalis. GitHub): GitHubGitHub stars
  • Machine Learning Design Patterns (V. Lakshmanan, S. Robinson, M. Munn. 2020. O'Reilly): Book | GitHubGitHub stars

Data Science with Python

  • ⭐ Python Data Science Handbook (J. Vanderplas. 2018. O'Reilly Media): Book | GitHubGitHub stars | Web
  • ⭐ Python for Data Analysis (W. McKinney. 2018. O'Reilly Media): Book | GitHubGitHub stars | Web
  • Python Data Analytics (F. Nelli. 2018. Apress): Book | GitHubGitHub stars
  • Data Analysis and Visualization Using Python (O. Embarak. 2018. Apress): Book | GitHubGitHub stars

Machine Learning with Python

  • ⭐ Introduction to Machine Learning with Python (A.C. Muler & S. Guido. 2017. O'Reilly Media): Book | GitHubGitHub stars | Web
  • Practical Machine Learning with Python (D. Sarkar, R. Bali, and T. Sharma. 2018. Apress): Book | GitHubGitHub stars
  • Machine Learning Applications Using Python (P. Mathur. 2019. Apress): Book | GitHubGitHub stars

Deep Learning with Python

  • ⭐ Deep Learning with Applications Using Python (N.K. Manaswi, 2018. Apress): Book | GitHubGitHub stars
  • ⭐ Dive into Deep Learning - NumPy/MXNet and PyTorch implementations (Aston Zhang, 2020): Book | GitHubGitHub stars
    • Dive into Deep Learning Compiler (Aston Zhang, 2020): Book | GitHubGitHub stars
  • ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book

Computer Vision with Python

  • ⭐ Computer Vision with Python 3 (S. Kapur, 2017. Packt): Book | GitHubGitHub stars
  • Programming Computer Vision with Python: Tools And Algorithms For Analyzing Images (Jan Erik Solem, 2012. O'Reilly): Book
  • Modern Computer Vision with PyTorch (V Kishore Ayyadevara, 2020. Packt): Book | GitHub

All with C++

Basic C++ Books

Machine Learning with C++

  • Hands-On Machine Learning with C++ (K. Kolodiazhnyi, 2020-05. Packt): Book | GitHubGitHub stars

Deep Learning with C++

  • C++ Implementation of PyTorch Tutorials for Everyone: GitHubGitHub stars

Image Processing & Computer Vision with C++

  • Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library: Book | GitHub
  • The CImg Library is a small and open-source C++ toolkit for image processing: Web

Courses

  • ⭐ Belajar Machine Learning Lengkap Dari Nol Banget sampai Practical - WiraD.K. Putra (2020): YouTube | GitHub
  • Neural networks - University De Sherbrooke by Hugo Larochelle (2013): YouTube | Web
  • ⭐ Standford Machine Learning - Standford by Andrew Ng (2008): YoutTube
  • ⭐ Caltech Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014): Web
  • ⭐ Carnegie Mellon University Deep Learning - CMU by: YouTube | Web
  • ⭐ Deeplearning.ai Neural Networks and Deep Learning - Deeplearning.ai by Andrew Ng in YouTube (2010-2014): YouTube
  • Standford Neural Networks and Deep Learning - Standford by Fei-Fei Li: YouTube: 2017
  • MIT Deep Learning - MIT by Lex Fridman: GitHubGitHub stars | YouTube
  • Stanford Deep Learning - Stanford by Andrew Ng: Homepage | Web | Coursera | GitHubGitHub stars
  • ⭐ Deep Learning with PyTorch - by sentdex: YouTube
  • Computer Vision - Univ. Central Florida by Mubarak Shah YouTube

Deep Learning Frameworks

  • Deep Learning with TensorFlow (G. Zaccone & Md.R. Karim, 2018. Packt): Book, Code, and GitHubGitHub stars
  • Deep Learning with PyTorch 1.0 (S. Yogesh K, 2019. Packt): Book and Code
  • ⭐ Deep Learning with PyTorch (V. Subramanian, 2018. Packt): Book and GitHubGitHub stars
  • ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book, Code
  • Deep Learning with Keras (S. Pal & A. Gulli, 2017. Packt): Book and CodeGitHub stars

PyTorch Frameworks

Network Programming

  • ⭐ Foundations of Python Network Programming (Brandon Rhodes. 2014. Apress): Book | GitHub GitHub stars

Research Groups

Universities

Corporations

Brain Team - Google AI: TensorFlow, GitHub Google AI Research | Facebook AI: PyTorch, GitHub Facebook Research | Microsoft AI: Microsoft Cognitive Toolkit (CNTK), GitHub Microsoft AI | Amazon AI: Alexa | Apple AI | Alibaba AI: GitHub Alibaba AI | IBM AI | Nvidia AI: GitHub Nvidia AI | Tencent AI: GitHub Tencent AI

Ph.D. in Machine Learning

Machine Learning - Carnegie Mellon University | EECS - University of California — Berkeley | Computer Science - Stanford University | EECS - Massachusetts Institute of Technology | Computer Science - Cornell University

Products

Self-driving Car: Tesla | Waymo | Industrial Autonomy & Robotics: Anki | Mov.ai | AI: Ultralytics LLC | FirmAI | deepdetect.com

AI Start-Up in Indonesia

Datasets

cvpapers.com | wikipedia.org | datasetlist.com | deeplearning.net | datahub.io | towardsai.net | medium-towards-artificial-intelligence

Vehicle Classification

  • Vehicle image database - Universidad Politécnica de Madrid (UPM) by J. Arróspide (2012) - 3425 images of vehicle rears: Web

Object Detection & Recognition

  • CIFAR10 [10] - University of Toronto by Alex Krizhevsky (2009): Raw (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck) | pdf
  • PASCAL VOC [20] - M. Everingham (2012): Raw (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
  • COCO [80] - COCO Consortium by Tsung-Yi Lin, et. al. (2015): Web | Download (80 classes: person & accessory, animal, vehicle, aoutdoor objects, sports, kitchenware, food, furniture, appliance, electronics, and indoor objects) | pdf
  • CIFAR100 [100] - University of Toronto by Alex Krizhevsky (2009): Raw (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
  • ImageNet [10,000] Stanford University by Olga Russakovsky (2012) - Raw | pdf

Object Tracking

  • KITTI [2]: Raw(2 classes: car & pedestrian) | pdf
  • LaSOT [85]: A High-quality Large-scale Single Object TrackingBenchmark - Stony Brook University by Heng Fan (2020): Raw (85 classes) | pdf
  • MOT16: A Benchmark for Multi-Object Tracking - Univ. of Adelaide by A. Milan, et. al. (2016)]: Raw | pdf
  • TAO [833]: A Large-Scale Benchmark for Tracking Any Object - Carnegie Mellon University by Achal Dave (2020): Raw (833 classes) | pdf

Monocular 3D Object Detection

  • KITTI Dataset - University of Tübingen by Andreas Geiger (2012): Raw | Object 2D | Object 3D | Bird's Eye View (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 | 2D Box | 3D Box | Realtime | Paper (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 | Toolbox | Paper (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 | Toolbox | Paper (8 classes: car, truck, bus, on rails, motorcycle, bicycle, caravan, and trailer)

Tools

  • docker.com: build and ship apps.
  • comet.ml: track, compare, explain and optimize experiments and models.
  • onnx.ai: open format built to represent machine learning models.
  • mlflow.org: an open source platform for the machine learning lifecycle.
  • cortex.dev: the open source stack for machine learning engineering.
  • mlperf.org: Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.

===

  • 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: A hyperparameter optimization framework.
  • Determined: Deep Learning Training Platform.
  • cuDF: GPU DataFrame Library

===

  • Netron: a viewer for neural network, deep learning and machine learning models.
  • playground: Deep playground is an interactive visualization of neural networks, written in TypeScript using d3.js.
  • 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: 3D visualization of convolutional neural network.
  • PyTorchViz: A small package to create visualizations of PyTorch execution graphs and traces.

===

  • 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.

Interested Research

  • Deep Learning Models - A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks: GitHub GitHub stars
  • Hyperparameter Optimization of Machine Learning Algorithms - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear): GitHub GitHub stars
  • FairMOT - A simple baseline for one-shot multi-object tracking: GitHub GitHub stars

Autonomous Vehicles

  • Awesome Autonomous Vehicles - manfreddiaz: GitHub GitHub stars
  • Autoware - Integrated open-source software for urban autonomous driving: Web | GitHub GitHub stars
  • CARLA Simulator - Open-source simulator for autonomous driving research: GitHub GitHub stars

Benchmark

benchmarks.ai | dawn.cs.stanford.edu | mlperf.org | MobilePhone - ai-benchmark.com | GitHub > deep-learning-benchmark - u39kun, DeepBench - baidu-research

Create Datasets

Journals, Magazines, and People

Journals

Magazines: towardsdatascience | paperswithcode | distill | xenonstack | awesomeopensource.com | emerge-ai.com

People

Podcast

Conferences for Image Processing & Computer Vision: guide2research.com | openaccess.thecvf.com