They also use a lot of electricity, which can increase costs and isn’t good for the environment.
Example: Training a deep learning model for voice recognition could take weeks on a regular computer, but a GPU can speed it up.
Deep learning works best when it has a lot of data to learn from. Unlike traditional methods, which austria telegram data can sometimes work with small datasets, deep learning needs huge amounts of data to perform well.
Why it’s a problem:
Collecting and preparing large datasets takes time and effort.
In some cases, like medical research, it’s hard to get enough data because of privacy rules or limited availability.
Example: A deep learning model for detecting diseases needs thousands of X-rays to learn. If there aren’t enough images, the model might not work well.
3. Hard to Understand
Deep learning models are like a “black box.” This means it’s difficult to figure out why the model made a certain decision.
Why it’s a problem:
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