In which fields is the Torch framework mainly used?

Torch is primarily used in the following fields:

1. Machine learning and deep learning: Torch is an open-source machine learning library that offers a rich set of tools and functions for building neural networks and conducting deep learning. Many researchers and data scientists use Torch to develop and train various machine learning models.
2. Computer Vision: Torch is widely used in the field of computer vision, where researchers and engineers utilize it to build and train various visual recognition and image processing models, such as object detection, image segmentation, and image classification.
3. Torch is widely popular in the natural language processing field, with many researchers and engineers using it to build and train various models for tasks such as text classification, machine translation, and sentiment analysis.
4. Reinforcement Learning: Torch is also used in the field of reinforcement learning, with many researchers and engineers using it to build and train various reinforcement learning models, such as Deep Q Networks (DQN) and policy gradient methods.

In general, Torch is primarily used in machine learning, deep learning, and related fields of artificial intelligence. It is a powerful and flexible tool that can assist researchers and engineers in quickly developing and experimenting with various models and algorithms.