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!)

L’issue non indica file, test o punti di ingresso e non fornisce implementazioni concrete di benchmark. Inizia esaminando le convenzioni esistenti del repository per i benchmark, quindi chiarisci gli algoritmi e le implementazioni incluse nell’ambito; per considerare il lavoro completato sarebbe necessario concordare un insieme di benchmark corrispondenti per training e inference.