Module 7- part 4.2- Python: Multivariate timeseries with CNN, RNN, LSTM, GRU
Автор: Pedram Jahangiry
Загружено: 2025-03-06
Просмотров: 1085
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
4- RNN python intuition and Univariate timeseries forecasting
5- Multivariate forecasting with RNN in python (this video)
Lecture timestamps:
00:00 where to find the materials
02:56 Multivariate example
20:43 applying DNN, RNN and LSTM in Python
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
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