Module 6- part1- Neural Network basics for timeseries forecasting скачать в хорошем качестве

Module 6- part1- Neural Network basics 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 (this video) 2- Deep Learning regularization and going beyond DNN for timeseries forecasting 3- DNN for timeseries forecasting in Python Lecture timestamps: 00:00 recap and roadmap 00:05:44 Starting from a simple example 00:12:09 Forward propagation 00:13:02 What are Neurons 00:15:18 Activation function 00:28:23 Building a Neural Network 00:41:20 Backward propagation 00:48:40 Loss optimization (GD, SGD, minibatch-GD) 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