Add benchmarks for machine learning application

Today, Machine learning applications are important use-cases of Python.
I haven't prepared concrete benchmark implementations yet, but I would like to suggest guidelines for machine learning benchmarks.

A. Each benchmark should provide all of the following implementations and shows the same result.

• Pure Python-based implementation (might be not easy). Sympy based implementation
• Numpy-based implementation.
• (optional) Famous frameworks like scikit-learn, TensorFlow, or PyTorch-based implementation.

B. Following algorithm-based benchmark should provide training and inference benchmark.

• Regression algorithm
• Decision tree algorithm
• Clustering algorithm
• Nearest neighborhood algorithm
• Matrix factorization
• ... (Please suggest!)

C. Deep learning-based or neural network-based benchmarks only provide inference benchmark with fixed weights since training benchmark needs GPU resources but using GPU resource is out of the topic.

• Simple neural network
• ... (Please suggest!)

Der Issue nennt keine Dateien, Tests oder Einstiegspunkte und enthält keine konkreten Benchmark-Implementierungen. Beginne damit, die bestehenden Benchmark-Konventionen des Repositorys zu prüfen, und kläre anschließend die Algorithmen und Implementierungen, die im Umfang liegen; für den Abschluss wäre ein vereinbarter Satz aufeinander abgestimmter Training- und Inference-Benchmarks erforderlich.