Deep Learning For Dummies® by John Paul Mueller & Luca Massaron

Author:John Paul Mueller & Luca Massaron [Mueller, John Paul & Massaron, Luca]
Language: eng
Format: epub
Published: 2019-05-14T00:00:00+00:00

Using online learning

Neural networks are more flexible than other machine learning algorithms, and they can continue to train as they work on producing predictions and classifications. This capability comes from optimization algorithms that allow neural networks to learn, which can work repeatedly on small samples of examples (called batch learning) or even on single examples (called online learning). Deep learning networks can build their knowledge step by step and remain receptive to new information that may arrive (like a baby’s mind, which is always open to new stimuli and to learning experiences).

For instance, a deep learning application on a social media website can train on cat images. As people post photos of cats, the application recognizes them and tags them with an appropriate label. When people start posting photos of dogs on the social network, the neural network doesn’t need to restart training; it can continue by learning images of dogs as well. This capability is particularly useful for coping with the variability of Internet data. A deep learning network can be open to novelty and adapt its weights to deal with it.

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