Tools/Libraries

- Compare strengths and weaknesses of TensorFlow and PyTorch

TensorFlow has static computational graphs, eager execution in PyTorch. TF has higher level APIs, PyTorch more flexible. TensorFlow better for production, PyTorch for research.

I leverage TensorFlow for production-ready models at scale given its features like eager execution, distributed training, and TF Serving. PyTorch is better for research prototyping where flexibility and debugging matter more. Integrations with Python tools like autograd and dynamic neural networks are ideal for R&D.

- Explain how you pre-process data with pandas and NumPy

Use Pandas for cleaning, munging, slicing data. NumPy for numerical operations like standardization. Can impute missing values, encode categoricals, normalize features.

- Explain experience with scikit-learn library

I have extensive experience using the scikit-learn library for a variety of machine learning tasks, including both supervised and unsupervised learning models. I've leveraged its powerful tools to build, train, and evaluate models for classification, regression, clustering, and dimensionality reduction.

The library's easy-to-understand API and rich set of functionalities make it a go-to choice for quick prototyping and experimentation. I've also combined scikit-learn with other libraries like NumPy and Pandas to preprocess data and evaluate model performance, making it an integral part of my data science toolkit.