What is the purpose of the dropout process in the fully connected layers of a neural network?
The purpose of the dropout process in the fully connected layers of a neural network is to prevent overfitting and improve generalization. Overfitting occurs when a model learns the training data too well and fails to generalize to unseen data. Dropout is a regularization technique that addresses this issue by randomly dropping out a fraction
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Training a neural network to play a game with TensorFlow and Open AI, Training model, Examination review
What is dropout and how does it help combat overfitting in machine learning models?
Dropout is a regularization technique used in machine learning models, specifically in deep learning neural networks, to combat overfitting. Overfitting occurs when a model performs well on the training data but fails to generalize to unseen data. Dropout addresses this issue by preventing complex co-adaptations of neurons in the network, forcing them to learn more
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 2, Examination review
How can overfitting be mitigated during the training process of an image classifier?
Overfitting is a common problem that occurs during the training process of an image classifier in the field of Artificial Intelligence. It happens when a model learns the training data too well, to the point that it becomes overly specialized and fails to generalize to new, unseen data. This can lead to poor performance and
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Building an image classifier, Examination review