Handling Different Data Formats in PyTorch

 Deep learning models often deal with diverse data types, such as images, text, and audio. 

For structured data, use libraries like pandas to read CSV files, then convert them into PyTorch tensors. For images, torchvision offers transforms to preprocess and load them easily. Numpy arrays can be directly converted to tensors. Text data can be tokenized and encoded using libraries like NLTK or SpaCy before conversion.  

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