Deep Learning With Tensorflow 2.0, Keras and Python - Dropout Regularization | Deep Learning Tutorial 20 (Tensorflow2.0, Keras & Python)

Overfitting and underfitting are common phenomena in the field of machine learning and the techniques used to tackle overfitting problem is called regularization. In deep learning, dropout regularization is used to randomly drop neurons from hidden layers and this helps with generalization. In this video, we will see a theory behind dropout regularization. We will then implement artificial neural network for binary classification problem and see how using dropout layer can increase the performance of the model. #dropoutregularization #dropoutregularizationtechnique #dropoutregularisation #deeplearning #deeplearningtutorial #dropoutdeeplearning Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses. Code: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/13_dropout_layer/dropout_regularization_ann.ipynb CSV file: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/13_dropout_layer Next video: https://www.youtube.com/watch?v=JnlM4yLFNuo&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=21 Previous video: https://www.youtube.com/watch?v=2osIZ-dSPGE&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=19 Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO Machine learning playlist : https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw Prerequisites for this series:    1: Python tutorials (first 16 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0     2: Pandas tutorials(first 8 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy 3: Machine learning playlist (first 16 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw   Website: https://codebasics.io/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.