Compare and contrast TensorFlow and Keras as popular frameworks for implementing neural networks. Discuss their strengths and limitations.

TensorFlow and Keras are two popular frameworks used for implementing neural networks. While TensorFlow is a comprehensive deep learning library, Keras is a high-level neural networks API that runs on top of TensorFlow. Let's compare and contrast these frameworks, discussing their strengths and limitations. TensorFlow: 1. Flexibility and Low-Level Control: TensorFlow offers great flexibility and low-level control over the neural network models. It provides a computational graph abstraction, allowing users to define and manipulate the graph operations directly. This makes it suitable for advanced research and complex model architectures. 2. Large Community and Ecosystem: TensorFlow has a large and active community, making it easy to find resources, tutorials, and support. It also has a rich ecosystem with various libraries and tools built on top of it, enabling tasks like computer vision, natural language processing, and reinforcement learning. 3. Scalability and Production Deployment: TensorFlow is designed to scale seamlessly across multiple devices and machines. It supports distrib....