Description: The Sequential Model in TensorFlow is a fundamental structure for building neural networks, characterized by being a linear stack of layers where each layer has exactly one input tensor and one output tensor. This model is intuitive and easy to use, making it a popular choice for those new to deep learning. In this approach, layers are stacked one after another, allowing the output of one layer to become the input of the next. This simplifies the model design process, as users can add layers sequentially, such as dense layers, convolutional layers, or activation layers, without worrying about the complexity of connections between them. The Sequential Model is ideal for tasks where the network architecture is linear and does not require branching or complex connections. Its simplicity and clarity make it accessible for beginners, while its flexibility allows experts to create sophisticated models efficiently. Additionally, TensorFlow provides tools and functions that facilitate the implementation and training of these models, contributing to their popularity in the machine learning community.
