This video covers early stopping in PyTorch! In this video, we will guide you through the implementation and understanding of early stopping, a powerful technique to prevent overfitting in deep learning models. Overfitting is a common problem where a model learns the training data too well, including its noise and outliers, thus failing to generalize well to unseen data. By using early stopping, we can monitor the model's performance on a validation dataset and halt the training process once the model starts to overfit, thereby saving both time and computational resources. Whether you're a beginner or an experienced practitioner in deep learning, this video will walk you through the code, the theory, and the practical tips to make your models more robust and efficient. Code for This Video: https://github.com/jeffheaton/app_deep_learning/blob/main/t81_558_class_03_4_early_stop.ipynb ~~~~~~~~~~~~~~~ COURSE MATERIAL ~~~~~~~~~~~~~~~ 📖 Textbook - Coming soon 😸🐙 GitHub - https://github.com/jeffheaton/app_deep_learning/ ▶️ Play List -    • 2023 PyTorch Version Applications of ...   🏫 WUSTL Course Site - https://sites.wustl.edu/jeffheaton/t81-558/ ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: https://www.heatonresearch.com/ 🐦 Twitter - https://twitter.com/jeffheaton 😸🐙 GitHub - https://github.com/jeffheaton 📸 Instagram - https://www.instagram.com/jeffheatondotcom/ 🦾 Discord: https://discord.gg/3bjthYv ▶️ Subscribe: null ~~~~~~~~~~~~~~ SUPPORT ME 🙏~~~~~~~~~~~~~~ 🅿 Patreon - https://www.patreon.com/jeffheaton 🙏 Other Ways to Support (some free) - https://www.heatonresearch.com/support.html ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #PyTorch #EarlyStopping #DeepLearning #Overfitting #ModelTraining #MachineLearning #AI #NeuralNetworks #DataScience #Validation #EfficientTraining #RobustModeling