Welcome to our tutorial on batch normalization in PyTorch, a transformative technique that has become a standard in training deep neural networks! Batch normalization helps in stabilizing and accelerating the training process by normalizing the inputs or activations within a mini-batch to have a mean of 0 and a standard deviation of 1. This normalization process helps in reducing the internal covariate shift, which can lead to faster convergence and allows the use of higher learning rates. In this video, we will break down the theory behind batch normalization and guide you through its implementation in PyTorch. You'll learn when to apply batch normalization in your network, usually after the convolutional or fully connected layers but before the activation function. Code for This Video: https://github.com/jeffheaton/app_deep_learning/blob/main/t81_558_class_04_4_batch_norm.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 #BatchNormalization #DeepLearning #NeuralNetworks #ModelTraining #Convergence #LearningRates #CovariateShift #AI #MachineLearning #DataScience #Optimization