These are my quick and dirty notes trying to follow main Machine Learning, Computer Vision & Deep Learning references
- awesome. 😎 Awesome lists about all kinds of interesting topics. https://github.com/sindresorhus/awesome
- awesome-awesomeness. A curated list of awesome awesomeness. https://github.com/bayandin/awesome-awesomeness
- awesome-python. A curated list of awesome Python frameworks, libraries, software and resources. https://github.com/vinta/awesome-python
- Website: https://awesome-python.com/
- awesome-github. A curated list of GitHub's awesomeness. https://github.com/phillipadsmith/awesome-github
- awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities. https://github.com/ChristosChristofidis/awesome-deep-learning
- awesome-deeplearning-resources. Deep Learning and deep reinforcement learning research papers and some codes. https://github.com/endymecy/awesome-deeplearning-resources
- awesome-machine-learning. A curated list of awesome Machine Learning frameworks, libraries and software. https://github.com/josephmisiti/awesome-machine-learning
- awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems. https://github.com/academic/awesome-datascience
- Awesome-Deep-Learning-Resources. Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier. https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources
- awesome-production-machine-learning. A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. https://github.com/EthicalML/awesome-production-machine-learning
- awesome-deep-learning-materials. Curated materials from Centroida's Deep Learning team. https://github.com/hggeorgiev/awesome-deep-learning-materials
- Awesome-Machine-Learning-DataScience_Resources. Curated Collection of Online and Free Resources for serious learning of Machine Learning and Data Science. https://github.com/rohan-paul/Awesome-Machine-Learning-DataScience_Resources
- awesome-ml-courses. Awesome free machine learning and AI courses with video lectures. https://github.com/luspr/awesome-ml-courses
- awesome-model-quantization. A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo. https://github.com/htqin/awesome-model-quantization
- awesome-hand-pose-estimation. Awesome work on hand pose estimation/tracking. https://github.com/xinghaochen/awesome-hand-pose-estimation
- https://www.learnpython.org/
- https://www.programiz.com/python-programming
- https://pythonsimplified.com/
- https://www.python.org/about/gettingstarted/
- TheAlgorithms/Python. All Algorithms implemented in Python. https://github.com/TheAlgorithms/Python
- The Data Science Trilogy: NumPy, Pandas and Matplotlib basics. https://towardsdatascience.com/the-data-science-trilogy-numpy-pandas-and-matplotlib-basics-42192b89e26
- the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. https://github.com/ritchieng/the-incredible-pytorch
- pytorch-beginner: pytorch tutorial for beginners. https://github.com/L1aoXingyu/pytorch-beginner
- pytorch-Deep-Learning: Deep Learning (with PyTorch). https://github.com/Atcold/pytorch-Deep-Learning
- d2l-en. Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 175 universities.https://github.com/d2l-ai/d2l-en
- Webiste: https://d2l.ai/
- pytorch-tutorial. PyTorch Tutorial for Deep Learning Researchers. https://github.com/yunjey/pytorch-tutorial
- EffectivePyTorch. PyTorch tutorials and best practices. https://github.com/vahidk/EffectivePyTorch
- Deep-Tutorials-for-PyTorch. In-depth tutorials for implementing deep learning models on your own with PyTorch. https://github.com/sgrvinod/Deep-Tutorials-for-PyTorch
- a-PyTorch-Tutorial-to-Object-Detection. SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection. https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection
- YOLO_v3_tutorial_from_scratch. Accompanying code for Paperspace tutorial series "How to Implement YOLO v3 Object Detector from Scratch". https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
- pytorch_notebooks: A collection of PyTorch notebooks for learning and practicing deep learning. https://github.com/omarsar/pytorch_notebooks
- MLWithPytorch: Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch. https://github.com/Mayurji/MLWithPytorch
- deepcourse: DeepCourse: Deep Learning for Computer Vision (from basics to State-of-the-Art). https://github.com/arthurdouillard/deepcourse
- UvA Deep Learning Tutorials: Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020. https://github.com/phlippe/uvadlc_notebooks
- neural networks with python and pytorch: http://datahacker.rs/neural-networks-with-python-and-pytorch/
Pytorch Lightning main repo: https://github.com/PyTorchLightning
- pytorch-lightning: The lightweight PyTorch wrapper for high-performance AI research. https://github.com/PyTorchLightning/pytorch-lightning
- lightning-transformers: Flexible interface for high performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.https://github.com/PyTorchLightning/lightning-transformers
- lightning-flash: Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning. https://github.com/PyTorchLightning/lightning-flash
- Example (1): Image Classification using Lightning Flash. https://medium.com/nerd-for-tech/image-classification-using-lightning-flash-e549b6c4285f
- lightning-bolts: Toolbox of models, callbacks, and datasets for AI/ML researchers. Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch. https://github.com/PyTorchLightning/lightning-bolts
- 4 PyTorch Lightning Community Computer Vision Examples To Inspire Your Next Project!. https://devblog.pytorchlightning.ai/4-pytorch-lightning-community-computer-vision-examples-to-inspire-your-next-project-70a5e3f10c8
- nanodet. ⚡Super fast and lightweight anchor-free object detection model. 🔥Only 1.8MB and run 97FPS on cellphone🔥. https://github.com/RangiLyu/nanodet
- insightface. Face Analysis Project on MXNet and PyTorch (2D and 3D Face Analysis Project). https://github.com/deepinsight/insightface
- Website: http://insightface.ai/
- vedadet. A single stage object detection toolbox based on PyTorch. https://github.com/Media-Smart/vedadet
- mmdetection. OpenMMLab Detection Toolbox and Benchmark. https://github.com/open-mmlab/mmdetection
- torchflare. TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly. https://github.com/Atharva-Phatak/torchflare
- pytorch-image-models: PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more. https://github.com/rwightman/pytorch-image-models
- torchinfo. Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary() API to view the visualization of the model. https://github.com/TylerYep/torchinfo
- Complex-YOLOv4-Pytorch: The PyTorch Implementation based on YOLOv4 of the paper: Complex-YOLO: Real-time 3D Object Detection on Point Clouds. https://github.com/maudzung/Complex-YOLOv4-Pytorch
- pytorchvideo: A deep learning library for video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms. https://github.com/facebookresearch/pytorchvideo
- ClassyVision. An end-to-end PyTorch framework for image and video classification. https://github.com/facebookresearch/ClassyVision
- detectron2: Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms. https://github.com/facebookresearch/detectron2
- mobile-vision: Mobile Computer Vision @ Facebook. https://github.com/facebookresearch/mobile-vision
- hydra: A framework for elegantly configuring complex applications. https://github.com/facebookresearch/hydra
- Example: hydra-tutorial: https://github.com/sscardapane/hydra-tutorial
- dino: PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO. https://github.com/facebookresearch/dino
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Model Garden for TensorFlow: The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. https://github.com/tensorflow/models
- Example (1): Training Faster R-CNN Using TensorFlow’s Object Detection API with a Custom Dataset. https://pub.towardsai.net/training-faster-r-cnn-using-tensorflow-object-detection-api-with-a-custom-dataset-88dd525666fd
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TensorFlow-Examples: TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2). https://github.com/aymericdamien/TensorFlow-Examples
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Collection of tutorials:
- Autoencoder in TensorFlow 2: Beginner’s Guide: https://learnopencv.com/autoencoder-in-tensorflow-2-beginners-guide/
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tensorflow-deep-learning. All course materials for the Zero to Mastery Deep Learning with TensorFlow course. https://github.com/mrdbourke/tensorflow-deep-learning
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eat_tensorflow2_in_30_days. Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋. https://github.com/lyhue1991/eat_tensorflow2_in_30_days
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paz: Hierarchical perception library in Python (for pose estimation, object detection, instance segmentation, keypoint estimation, face recognition). https://github.com/oarriaga/paz
- Website: https://oarriaga.github.io/paz/
- Adventures-in-TensorFlow-Lite: This repository contains notebooks that show the usage of TensorFlow Lite (TF Lite) for quantizing deep neural networks. https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite
- optuna: A hyperparameter optimization framework. https://github.com/optuna/optuna
- Website: https://optuna.org/
- Collection of unofficial optuna examples (scikit-learn, pytorch, tensorflow, xgboost,...) https://github.com/toshihikoyanase/optuna-examples
- tinyml-papers-and-projects: This is a list of interesting papers and projects about TinyML. https://github.com/gigwegbe/tinyml-papers-and-projects
- Deploy YOLOv5 to Jetson Xavier NX at 30FPS: https://blog.roboflow.com/deploy-yolov5-to-jetson-nx/. First, we have to train YOLO V5 (https://models.roboflow.com/object-detection/yolov5)
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MadeWithML: Learn how to responsibly deliver value with ML. https://github.com/GokuMohandas/MadeWithML
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applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. Curated papers, articles, and blogs on data science & machine learning in production. ⚙️. https://github.com/eugeneyan/applied-ml
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machine-learning-roadmap: A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. https://github.com/mrdbourke/machine-learning-roadmap
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machine-learning: Practical Full-Stack Machine Learning. https://github.com/BindiChen/machine-learning
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PyCaret: open-source, low-code machine learning library in Python that automates machine learning workflows. https://github.com/pycaret/pycaret
- Official Website: https://www.pycaret.org
- Documentation: https://pycaret.readthedocs.io/en/latest/
- Examples: https://github.com/pycaret/pycaret/tree/master/examples
- Example (1): Deploy Machine Learning Pipeline on the cloud using Docker Container. https://towardsdatascience.com/deploy-machine-learning-pipeline-on-cloud-using-docker-container-bec64458dc01
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Scikit-learn: machine learning in Python. https://github.com/scikit-learn/scikit-learn
- Website: https://scikit-learn.org
- Example (1): Build and Run a Docker Container for your Machine Learning Model. https://towardsdatascience.com/build-and-run-a-docker-container-for-your-machine-learning-model-60209c2d7a7f
- scikit-learn-mooc. scikit-learn course. https://github.com/INRIA/scikit-learn-mooc
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numpy-ml: Machine learning, in numpy. https://github.com/ddbourgin/numpy-ml
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vaex: Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀. Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. https://github.com/vaexio/vaex
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best-of-ml-python. 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. https://github.com/ml-tooling/best-of-ml-python
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ML-From-Scratch. Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. https://github.com/eriklindernoren/ML-From-Scratch
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Machine-Learning-Collection. A resource for learning about ML, DL, PyTorch and TensorFlow. https://github.com/aladdinpersson/Machine-Learning-Collection
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ML-course. A machine learning course using Python, Jupyter Notebooks, and OpenML. https://github.com/ML-course/master
- Webiste: https://ml-course.github.io/
- statsmodels: statistical models, hypothesis tests, and data exploration. https://github.com/statsmodels/statsmodels. Example of use: Time Series Decomposition In Python (Time Series Analysis Made Easy): https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2
- Multiple Time Series Forecasting with PyCaret: https://towardsdatascience.com/multiple-time-series-forecasting-with-pycaret-bc0a779a22fe
- greykite. A flexible, intuitive and fast forecasting library. https://github.com/linkedin/greykite
- 130 Machine Learning Projects Solved and Explained: https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392
- ML-ProjectYard. This repo consists of multiple machine learning based projects with frontend. This is the one stop open-source destination for all the projects based on Machine Learning, Deep Learning, NLP,Computer Vision and everything includes frontend and backend!. https://github.com/ashishsahu1/ML-ProjectYard
- featurewiz. Use advanced feature engineering strategies and select the best features from your data set fast with a single line of code. Featurewiz is a new python library for creating and selecting the best features in your data set fast!. https://github.com/AutoViML/featurewiz
- Example: Automate your Feature Selection Workflow in one line of Python code. https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4
- Titanic: Machine Learning from Disaster - EDA: https://www.kaggle.com/dwin183287/titanic-machine-learning-from-disaster-eda
- 4 Machine learning techniques for outlier detection in Python. https://towardsdatascience.com/4-machine-learning-techniques-for-outlier-detection-in-python-21e9cfacb81d
- awesome-datascience. 📝 An awesome Data Science repository to learn and apply for real world problems. https://github.com/academic/awesome-datascience
- Data-science-best-resources. Carefully curated resource links for data science in one place. https://github.com/tirthajyoti/Data-science-best-resources
- data-scientist-roadmap. Toturial coming with "data science roadmap" graphe. https://github.com/MrMimic/data-scientist-roadmap
- Data Science Collected Resources. A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place. https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md
- DataScienceResources. Open Source Data Science Resources. https://github.com/jonathan-bower/DataScienceResources
- Interview-Prepartion-Data-Science. https://github.com/krishnaik06/Interview-Prepartion-Data-Science
- A Complete Road map to Deep learning 2021 – Part 1. https://prabakaranchandran.com/2021/04/26/a-complete-road-map-to-deep-learning-2021-part-1/
- Deep-Learning. Study and implementation about deep learning models, architectures, applications and frameworks. https://github.com/arnaldog12/Deep-Learning
- gradio: Create UIs for prototyping your machine learning model in 3 minutes. Quickly create customizable UI components around your models. https://github.com/gradio-app/gradio
- Example (1): Supercharge Your Machine Learning Experiments with PyCaret and Gradio. https://towardsdatascience.com/supercharge-your-machine-learning-experiments-with-pycaret-and-gradio-5932c61f80d9
- streamlit. The fastest way to build and share data apps. https://github.com/streamlit/streamlit
- Example (1): Deploying Your Machine Learning Apps in 2021 (Streamlit Sharing is here and is awesome). https://towardsdatascience.com/deploying-your-machine-learning-apps-in-2021-a3471c049507
- Example (2): Diabetes Prediction App. https://github.com/arunnthevapalan/diabetes-prediction-app
- fastapi: FastAPI framework, high performance, easy to learn, fast to code, ready for production. https://github.com/tiangolo/fastapi
- Website: https://fastapi.tiangolo.com/
- Example (1): How to deploy Machine Learning models as a Microservice using FastAPI. https://towardsdatascience.com/how-to-deploy-machine-learning-models-as-a-microservice-using-fastapi-b3a6002768af
- Deep-Learning-in-Production: In this repository, I will share some useful notes and references about deploying deep learning-based models in production. https://github.com/ahkarami/Deep-Learning-in-Production
- tfx. TFX is an end-to-end platform for deploying production ML pipelines. When you’re ready to move your models from research to production, use TFX to create and manage a production pipeline. https://github.com/tensorflow/tfx.
- Website: https://www.tensorflow.org/tfx
- awesome-mlops: A curated list of references for MLOps. https://github.com/visenger/awesome-mlops
- Website: https://ml-ops.org/
- awesome-mlops: 😎 A curated list of awesome MLOps tools. https://github.com/kelvins/awesome-mlops
- Reproducible Deep Learning. Host repository for the "Reproducible Deep Learning" PhD course. https://github.com/sscardapane/reprodl2021
- mlflow. Open source platform for the machine learning lifecycle. https://github.com/mlflow/mlflow/
- Website. https://mlflow.org/
- Example: Easy MLOps with PyCaret + MLflow. https://www.kdnuggets.com/2021/05/easy-mlops-pycaret-mlflow.html
- Website. https://mlflow.org/
- pachyderm. Reproducible Data Science at Scale!. Data Versioning, Data Pipelines, and Data Lineage. Tool for version-controlled, automated, end-to-end data pipelines for data science. https://github.com/pachyderm/pachyderm
- Website: https://www.pachyderm.com/
- mltrace. Coarse-grained lineage and tracing for machine learning pipelines. https://github.com/loglabs/mltrace
- Tool-Experimentation. Objective of the repository to play around with different tools (keepsake, MLflow etc) with basic projects. https://github.com/Mayurji/Tool-Experimentation
- How I Build Machine Learning Apps in Hours: https://pub.towardsai.net/how-i-build-machine-learning-apps-in-hours-a1b1eaa642ed?gi=7cbefed10ea6
- How to Dockerize Any Machine Learning Application: https://towardsdatascience.com/how-to-dockerize-any-machine-learning-application-f78db654c601. https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html
- Deploying Your Machine Learning Apps in 2021: https://towardsdatascience.com/deploying-your-machine-learning-apps-in-2021-a3471c049507
- aim. Aim — a super-easy way to record, search and compare 1000s of ML training runs. https://github.com/aimhubio/aim
- Website: https://aimstack.io/
- papermill. 📚 Parameterize, execute, and analyze notebooks. https://github.com/nteract/papermill
- vertex-ai. Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified AI platform. https://cloud.google.com/vertex-ai
- nbdime: Tools for diffing and merging of Jupyter notebooks. https://github.com/jupyter/nbdime
- nbQA: Run any standard Python code quality tool on a Jupyter Notebook. https://github.com/nbQA-dev/nbQA
- jupyter-nbrequirements: Dependency management and optimization in Jupyter Notebooks. https://github.com/thoth-station/jupyter-nbrequirements
- watermark. An IPython magic extension for printing date and time stamps, version numbers, and hardware information. https://github.com/rasbt/watermark
- 10 Useful Jupyter Notebook Extensions for a Data Scientist (Qgrid, itables, jupyter-datatables, ipyvolume, bqplot, livelossplot, TensorWatch, Polyaxon, handcalcs, jupyternotify). https://towardsdatascience.com/10-useful-jupyter-notebook-extensions-for-a-data-scientist-bd4cb472c25e
- pipenv. Python Development Workflow for Humans. https://github.com/pypa/pipenv
- cookiecutter-data-science. A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. https://github.com/drivendata/cookiecutter-data-science
- kaggle-solutions. 🏅 Collection of Kaggle Solutions and Ideas 🏅. https://github.com/faridrashidi/kaggle-solutions
- Website: https://farid.one/kaggle-solutions/
- Complete collection of the weekly series showcasing underrated Kaggle Notebooks. https://www.kaggle.com/headsortails/notebooks-of-the-week-hidden-gems
- Hidden Gems: A Collection of Underrated Notebooks. https://www.kaggle.com/headsortails/hidden-gems-a-collection-of-underrated-notebooks/
- Embedding and Linking Notebooks. https://www.kaggle.com/product-feedback/230748
- Jupyter Notebooks and other material from tutorial sessions on Machine Learning, Data Science, and related. https://github.com/dennisbakhuis/Tutorials
- awesome-Python-data-science-books. Probably the best curated list of data science books in Python. https://github.com/khuyentran1401/awesome-Python-data-science-books
- dlwpt-code: Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann. https://github.com/deep-learning-with-pytorch/dlwpt-code
- Modern Computer Vision with PyTorch. https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch
- Practical Data Analysis using Jupyter Notebook. https://github.com/PacktPublishing/Practical-Data-Analysis-using-Jupyter-Notebook
- Advanced Deep Learning with TensorFlow 2 and Keras. https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
- Practical-Data-Science-with-Python. https://github.com/PacktPublishing/Practical-Data-Science-with-Python
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fiftyone: The open-source tool for building high-quality datasets and computer vision models. https://github.com/voxel51/fiftyone
- Website: https://voxel51.com/docs/fiftyone/
- Example (1): Stop Wasting Time with PyTorch Datasets! (Guide to speeding up and streamlining your dataset workflows with FiftyOne). https://towardsdatascience.com/stop-wasting-time-with-pytorch-datasets-17cac2c22fa8
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knowyourdata. A tool to help researchers and product teams understand datasets with the goal of improving data quality, and mitigating fairness and bias issues. Know Your Data helps researchers, engineers, product teams, and decision makers understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues. https://github.com/PAIR-code/knowyourdata.
- Website: https://knowyourdata.withgoogle.com/
- deep-learning-model-convertor. The convertor/conversion of deep learning models for different deep learning frameworks/softwares. https://github.com/ysh329/deep-learning-model-convertor
- colab_everything: Python library to run streamlit, flask, fastapi, etc on google colab. https://github.com/Ankur-singh/colab_everything
- colab_utils: Some useful (or not so much) Python stuff for Google Colab notebooks. https://github.com/ricardodeazambuja/colab_utils
- mediapipe. Cross-platform, customizable ML solutions for live and streaming media. https://github.com/google/mediapipe
- DeepLearningExamples. NVIDIA Deep Learning Examples for Tensor Cores. https://github.com/NVIDIA/DeepLearningExamples
- AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
- ThinamXx/300Days__MachineLearningDeepLearning: Journey of 300DaysOfData in Machine Learning and Deep Learning. https://github.com/ThinamXx/300Days__MachineLearningDeepLearning
- jelifysh/100daysofmlcode: https://github.com/jelifysh/100daysofmlcode/
- Intro to pandas: https://colab.research.google.com/notebooks/mlcc/intro_to_pandas.ipynb
- TensorFlow Programming Concepts: https://colab.research.google.com/notebooks/mlcc/tensorflow_programming_concepts.ipynb
- First Steps with TensorFlow: https://colab.research.google.com/notebooks/mlcc/first_steps_with_tensor_flow.ipynb
- Intro to Neural Networks: https://colab.research.google.com/notebooks/mlcc/intro_to_neural_nets.ipynb
- Intro to Sparse Data and Embeddings: https://colab.research.google.com/notebooks/mlcc/intro_to_sparse_data_and_embeddings.ipynb
- Tensorflow with GPU: https://colab.research.google.com/notebooks/gpu.ipynb
- TPUs in Colab: https://colab.research.google.com/notebooks/tpu.ipynb
- Retraining an Image Classifier (using TF Hub): https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_image_retraining.ipynb
- Text Classification with Movie Reviews (using TF Hub): https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_text_classification.ipynb
- Open Source project to help predict Tuberculosis from chest X-Rays (Tensorflow, OpenCV, Scikit-Learn). https://github.com/darwinai/TuberculosisNet
- MaskTheFace: MaskTheFace is computer vision-based script to mask faces in images. https://github.com/aqeelanwar/MaskTheFace
- Face-Mask-Detection-Keras: Implementing a face mask detector using Keras and OpenCV. https://github.com/OchirnyamB/Face-Mask-Detection-Keras
- Real-time face alignment: evaluation methods, training strategies and implementation optimization (C++ code, but interesting). https://towardsdatascience.com/faster-smoother-smaller-more-accurate-and-more-robust-face-alignment-models-d8cc867efc5. https://gitlab.com/visualhealth/vhpapers/real-time-facealignment
- Automatic license plate recognition using YoloV5 and PyTesseract. https://github.com/sid0312/anpr_yolov5
- AI, Machine Learning and Analytics applied to Sports. https://github.com/fpretto/sports_analytics
- norfair. Lightweight Python library for adding real-time 2D object tracking to any detector. https://github.com/tryolabs/norfair
- yolov5-face. YOLO5Face: Why Reinventing a Face Detector. https://github.com/deepcam-cn/yolov5-face
- siam-mot. SiamMOT: Siamese Multi-Object Tracking. https://github.com/amazon-research/siam-mot
- HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation. HyperSeg - Official PyTorch Implementation: https://github.com/YuvalNirkin/hyperseg
- leafmap. A Python package for geospatial analysis and interactive mapping with minimal coding in a Jupyter environment. https://github.com/giswqs/leafmap
- paperswithcode datasets: https://paperswithcode.com/datasets
- awesome-public-datasets: A topic-centric list of HQ open datasets. https://github.com/awesomedata/awesome-public-datasets
- awesome-data. Awesome datasets curated. By topic and for core data. https://github.com/datasets/awesome-data
- Website: https://datahub.io/collections
- labelme. Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation). https://github.com/wkentaro/labelme
- labelImg. 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images. https://github.com/tzutalin/labelImg
- CVAT (Computer Vision Annotation Tool). Powerful and efficient Computer Vision Annotation Tool (CVAT). https://github.com/openvinotoolkit/cvat
- VoTT (Visual Object Tagging Tool). Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos. https://github.com/microsoft/VoTT
- Multi-object-Tracking-paper-code-list. https://github.com/nightmaredimple/Multi-object-Tracking-paper-code-list
- learn-python. 📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations. https://github.com/trekhleb/learn-python
- ds-cheatsheets. List of Data Science Cheatsheets to rule the world. https://github.com/FavioVazquez/ds-cheatsheets
- Your Ultimate Data Mining & Machine Learning Cheat Sheet (using scikit-learn). https://towardsdatascience.com/your-ultimate-data-mining-machine-learning-cheat-sheet-9fce3fa16
- Data-Science--Cheat-Sheet. Data Science (Cheat Sheets). https://github.com/georgearun/Data-Science--Cheat-Sheet
- Artificial Intelligence cheatsheets. VIP cheatsheets for Stanford's CS 221 Artificial Intelligence. https://github.com/afshinea/stanford-cs-221-artificial-intelligence
- Machine Learning cheatsheets. VIP cheatsheets for Stanford's CS 229 Machine Learning. https://github.com/afshinea/stanford-cs-229-machine-learning.
- Deep Learning cheatsheets. VIP cheatsheets for Stanford's CS 230 Deep Learning. https://github.com/afshinea/stanford-cs-230-deep-learning
- pytest. The pytest framework makes it easy to write small tests, yet scales to support complex functional testing. https://github.com/pytest-dev/pytest
- PyTest for Machine Learning — a simple example-based tutorial. https://towardsdatascience.com/pytest-for-machine-learning-a-simple-example-based-tutorial-a3df3c58cf8
- tirthajyoti/ai-ds: This is an image of a minimal Data Science stack with Python 3.8.5 on top of Ubuntu 20.04 LTS minimal image. https://hub.docker.com/r/tirthajyoti/ai-ds