Prompt Title: PyTorch use cases.

PyTorch is an open-source machine learning library used for developing and training deep learning models. Here are some of the use cases for PyTorch:

1. Computer vision: PyTorch is widely used in computer vision tasks such as object detection, image classification, and segmentation. PyTorch provides easy-to-use APIs for building and training convolutional neural networks (CNNs) and other deep learning models.

2. Natural language processing: PyTorch is also used in natural language processing (NLP) tasks such as text classification, sentiment analysis, and language translation. PyTorch offers various pre-trained language models such as BERT, GPT-2, and RoBERTa, which can be fine-tuned on specific NLP tasks.

3. Speech recognition: PyTorch can be used in speech recognition tasks such as speech-to-text conversion and voice-based command recognition. PyTorch provides various pre-trained models such as DeepSpeech, which can be fine-tuned on specific speech recognition tasks.

4. Recommender systems: PyTorch is also used in building recommender systems for personalized recommendations. PyTorch provides APIs for building deep learning models such as collaborative filtering, which can be used in recommender systems.

5. Reinforcement learning: PyTorch can be used in reinforcement learning tasks such as game playing and robotics. PyTorch provides APIs for building and training deep reinforcement learning models such as deep Q-learning.

Overall, PyTorch is a versatile library that can be used in various machine learning tasks and is widely adopted by researchers and practitioners in academia and industry.