Module 7- part 3- Deep Dive into Gated cells, LSTM for timeseries скачать в хорошем качестве

Module 7- part 3- Deep Dive into Gated cells, LSTM for timeseries

This module provides a comprehensive overview of fundamental concepts and techniques related to deep sequence modeling. We explore deep learning for timeseries data, highlighting the inadequacy of DNN and CNN architectures for this task and introducing the recurrent neural network (RNN) and later LSTM as a solution. we do this module in 4 parts (3 theory lectures and one Pythone part) 1- DNN vs RNN intuition 2- RNN deep dive 3- LSTM deep dive (this video) 4- RNN python intuition and Univariate timeseries forecasting 5- Multivariate forecasting with RNN in python Lecture timestamps: 00:00 Road map and big picture 07:36 Beyond RNN 10:11 How to solve vanishing gradient problem 20:08 Inside the LSTM cell 40:14 what are the 4 gates in LSTM, deep dive! 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