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AI Code Repositories for Learners
1. TensorFlow
Repository: tensorflow/tensorflow
Description: The official TensorFlow repository. It includes the TensorFlow library itself, along with tutorials, examples, and extensive documentation.
2. PyTorch
Repository: pytorch/pytorch
Description: The official PyTorch repository. It contains the PyTorch library, along with examples, tools, and documentation.
3. Keras
Repository: keras-team/keras
Description: The official Keras repository. It includes the Keras library for building and training neural networks, with code, documentation, and examples.
4. Scikit-learn
Repository: scikit-learn/scikit-learn
Description: The official Scikit-learn repository. It contains the Scikit-learn library, which provides simple and efficient tools for data mining and data analysis.
5. Fastai
Repository: fastai/fastai
Description: The Fastai library builds on top of PyTorch to simplify the process of building and training deep learning models. It includes numerous tutorials and practical applications.
6. Hugging Face Transformers
Repository: huggingface/transformers
Description: The Hugging Face Transformers library provides state-of-the-art natural language processing models. It includes pre-trained models for tasks like text classification, translation, and more.
7. OpenCV
Repository: opencv/opencv
Description: The official OpenCV repository. OpenCV is an open-source library for computer vision and image processing.
8. Awesome Machine Learning
Repository: josephmisiti/awesome-machine-learning
Description: A curated list of machine learning frameworks, libraries, and software. It includes resources for various languages and platforms.
9. Data Science Tutorials
Repository: data-science-at-microsoft/data-science-at-microsoft.github.io
Description: A collection of tutorials and notebooks for learning data science and machine learning, curated by Microsoft.
10. The Algorithms
Repository: TheAlgorithms/Python
Description: A collection of algorithms and data structures implemented in Python. It’s a great resource for understanding basic concepts and implementing algorithms.
11. MLflow
Repository: mlflow/mlflow
Description: MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment.
12. AutoML
Repository: automl/auto-sklearn
Description: Auto-sklearn is an open-source AutoML library based on Scikit-learn. It aims to automate the process of machine learning model selection and hyperparameter tuning.
13. AI Experiments by Google
Repository: googlecreativelab/ai-experiments
Description: A collection of interactive AI experiments and projects created by Google’s Creative Lab. Great for seeing AI in action and understanding its applications.
14. Deep Learning Specialization
Repository: coursera-deeplearning-specialization
Description: Repositories associated with the Deep Learning Specialization by Andrew Ng. It contains assignments and resources related to the course.
15. Papers with Code
Repository: paperswithcode/paperswithcode
Description: This repository contains a collection of machine learning papers along with their associated code implementations, organized by task and dataset.