Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Dropout

The video tutorial explains overfitting in machine learning, where a model learns the training data too well but fails to generalize to unseen data. It discusses how model complexity, defined by the number of parameters, can lead to overfitting. To address this, dropout is introduced as a technique to reduce overfitting by randomly freezing neurons during training, effectively training different models and combining their outputs. This approach is akin to ensemble learning. The video concludes with a preview of implementing dropout in PyTorch.

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