Mitigating Overfitting and Underfitting with Dropout, Regularization - Full Stack Deep Learning

Overfitting and Underfitting are very common problems faced when training Deep Learning Models. In this tutorial, we shall see how to solve these problems via Data augmentation, Data collection, dropout, regularization, early stopping, less complex models, hyperparameter tuning, normalization. Previous (Introductory): https://youtu.be/GW3VadqOUnU Previous (Tensors and Variables): https://youtu.be/Kg2OgVHSH2o1 Previous (Linear Regression for Car Price Prediction): https://youtu.be/Y7DsfKyBF7g Previous (Convolutional Neural Networks for Malaria Diagnosis): https://youtu.be/MthqOrx_1Gk Previous (Loading and Saving TensorFlow Model to Gdrive): https://youtu.be/0U7IimAAC5Y Previous (Functional API, Model Subclassing and Custom Layers): https://youtu.be/wQz36Cmhe40 Previous (Performance Measurement): https://youtu.be/rqCzLNKJEvY Previous (Callbacks with TensorFlow 2): https://youtu.be/E_Ipd2LzTBw Colab Notebook: https://colab.research.google.com/drive/1uUH-asz3CFxlvld8uGx-m3crr4Iepp3u#scrollTo=ATnj3IWceW69 Check out our Deep Learning with TensorFlow 2 course (https://www.neuralearn.ai/course_page/3/). Check out our Deep Learning for Computer Vision with TensorFlow 2 course (https://www.neuralearn.ai/course_page/5/). Check out our Complete Linear Algebra Course https://www.neuralearn.ai/course_page/1/ Feel free to ask any questions. Always stay updated https://www.neuralearn.ai/subscribe/ Connect with us here: Twitter: https://twitter.com/neulearndotai Facebook: linkhttps://www.facebook.com/Neuralearnai-107372484374170/ LinkedIn https://www.linkedin.com/company/neuralearn