Module 6 part 2- Deep Learning regularization and going beyond NN for timeseries forecasting скачать в хорошем качестве

Module 6 part 2- Deep Learning regularization and going beyond NN for timeseries forecasting

In this module, we will delve into fundamental concepts in deep learning for timeseries forecasting. we do this module in 3 parts: 1- Neural Network basics for timeseries forecasting 2- Deep Learning regularization and going beyond DNN for timeseries forecasting (this video) 3- DNN for timeseries forecasting in Python Lecture timestamps: 00:00:00 recap and roadmap 00:01:47 Handling overfitting 00:04:48 DL regularization techniques 00:08:50 Drop out regularization 00:12:02 Early Stopping 00:23:41 Data augmentation 00:26:35 NN playground 00:34:14 What is a Tensor? 00:49:35 Why should we go beyond NN architechture for timeseries forecasting? Relevant playlists: Deep Forecasting Concepts, simply explained:    • Deep Forecasting codes and concepts (Simpl...   Machine Learning Codes and Concepts:    • Machine Learning Codes and Concepts (Simpl...   Deep Learning Concepts, simply explained:    • Deep Learning Codes and Concepts (Simply E...   Instructor: Pedram Jahangiry All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own. https://github.com/PJalgotrader