The TensorFlow platform helps you implement best practices for data automation, model tracking, performance monitoring, and model retraining. Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success.

TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning and developing neural networks faster and easier.

Is TensorFlow Difficult to Learn? Up to an intermediate level, TensorFlow is not difficult to learn if you have a foundation in Python programming and you rely on high-level APIs such as Keras. A foundational knowledge of machine learning and neural networks in particular also helps tremendously.

Tensorflow is the most popular and apparently best Deep Learning Framework out there. Why is that? TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models (=algorithms) that use multi-layer neural networks.

According to users of TensorFlow and industry-experts, TensorFlow is hard to learn and somewhat difficult to use too. TensorFlow is ingrained with a lack of flexibility, but research is all about flexibility, that’s why TensorFlow is considered to be tough in learning.