Predictive Analytics with TensorFlow 8.3: Tuning CNN Hyperparameters

The video discusses the memory requirements of CNNs during training and inference, offering solutions for memory issues. It explains the use of max pooling layers versus convolutional layers and introduces the dropout technique to prevent overfitting. The RMS prop optimizer is also covered, detailing its function and parameters. The video concludes with a preview of a CNN-based predictive model for sentiment analysis.

Discuss the implications of overfitting in large neural networks.

What is the purpose of the RMS prop optimizer in CNN training?