Why is TensorFlow the proper library for Deep Learning? 1 pointIt has extensive built-in support for deep learning.It provides a collection of trainable mathematical functions that are useful for neural networks. It will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers.All of the above.

Which statement is FALSE about TensorFlow?1 pointTensorFlow is an open source library.TensorFlow library is not proper for handling Machine Learning Problems.TensorFlow has a C/C++ backend as well as Python modules.TensorFlow is well suited for handling Deep Learning Problems.All of the above.

What is the Machine Learning Python library that supports building Machine Learning models, as discussed in the project introduced in this section?a.scikit-learnb.PyTorchc.Kerasd.TensorFlow

How does PyTorch's dynamic computational graph and the Autograd module facilitate the process of building, modifying, and training neural networks compared to other machine learning libraries? Discuss the advantages and any potential challenges you might face. Additionally, provide examples of real-world applications where PyTorch can be particularly beneficial. Instructions:

3.Where can you learn more about TensorFlow?

Federated learning, transportation modeling, Python libraries (TensorFlow Federated, PyTorch),