How does Keras handle various deep learning tasks?

Keras is a user-friendly deep learning library that can be used to handle various deep learning tasks. Below are some common deep learning tasks and examples of how Keras handles them.

1. Image classification: Keras offers some pre-trained models like VGG, ResNet, and Inception that can be used for image classification tasks. You can either use these models or train a new one for image classification.
2. Object detection: Keras offers popular models for object detection such as YOLO, Faster R-CNN, and SSD. You can utilize these models to detect, localize, and classify objects in images.
3. Image segmentation: Keras also offers some image segmentation models like U-Net and Mask R-CNN. These models are capable of segmenting images into different regions and assigning a label to each region.
4. Text classification: Keras can be used to build text classification models, such as recurrent neural networks (RNN), long short-term memory networks (LSTM), and convolutional neural networks (CNN), etc. These models can be used to analyze and classify text data.
5. Sequence generation: Keras also supports tasks involving generating sequences, such as language modeling and machine translation. You can use models like RNN or Transformer to generate sequence data.

In general, Keras offers a wide range of tools and models to handle various deep learning tasks. You can choose the appropriate model and tools based on your needs to build and train deep learning models.