Скачать или смотреть PyTorch Dropout Regularization (4.3)

Welcome to our comprehensive tutorial on dropout in PyTorch, a vital regularization technique used in training neural networks! Dropout is designed to prevent overfitting, a common problem where a model performs well on the training data but poorly on unseen data. During training, dropout randomly sets a fraction of the input units to 0 at each update, helping the network to become less reliant on any specific feature, thereby promoting a more generalized model. In this video, we'll delve into the theoretical understanding of dropout and guide you through its implementation in PyTorch. You'll learn when and how to apply dropout in various layers of your neural network, ensuring that your models are more robust and perform well on both training and validation datasets.

Code for This Video:
https://github.com/jeffheaton/app_dee...

~~~~~~~~~~~~~~~ COURSE MATERIAL ~~~~~~~~~~~~~~~
📖 Textbook - Coming soon
😸🐙 GitHub - https://github.com/jeffheaton/app_dee...
▶️ Play List -    • 2025 PyTorch Version Applications of Deep ...  
🏫 WUSTL Course Site - https://sites.wustl.edu/jeffheaton/t8...



~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~
🖥️ Website: https://www.heatonresearch.com/
🐦 Twitter -   / jeffheaton  
😸🐙 GitHub - https://github.com/jeffheaton
📸 Instagram -   / jeffheatondotcom  
🦾 Discord:   / discord  
▶️ Subscribe: https://www.youtube.com/c/heatonresea...


~~~~~~~~~~~~~~ SUPPORT ME 🙏~~~~~~~~~~~~~~
🅿 Patreon -   / jeffheaton  
🙏 Other Ways to Support (some free) - https://www.heatonresearch.com/suppor...


~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#PyTorch #Dropout #Regularization #Overfitting #NeuralNetworks #DeepLearning #ModelTraining #MachineLearning #AI #RobustModeling #DataScience #Validation