Employ regularization techniques like dropout, early stopping, or L1/L2 regularization.All the time divide your data into training, validation, and testing sets.It occurs when your model learns the training data too well, a lot in order that it fails to generalize to unseen data. Overfitting is the bogeyman under the bed for each beginner in deep learning. You fed it a lot out of your training data, it couldn’t recognize anything.” ![]() ![]() Heartbroken, Alex sought Emma’s advice, to which she replied, “You’ve fallen for the Overfitting Monster. The model performed miserably on latest data. Elated, Alex shared his model with a senior colleague, Emma, just for her to return the subsequent day with bad news. In his enthusiasm, he trained a posh neural network that might predict the stock market with startling accuracy. Once upon a time, there was a rookie data scientist named Alex, desperate to apply his newly-acquired deep learning skills.
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