Training Autoregressive MLPs, the basics! PyTorch Deep Learning Tutorial
TIMESTAMPS:
0:00 - Introduction
1:02 - Explanation of sequential/time series data compared to single-point data.
2:00 - Introduction to predicting future max daily temperatures using historical data.
3:32 - Decision on fixed input size for the neural network.
4:41 - Explanation of auto-regressive predictor concept.
6:01 - Introduction to the dataset: max daily temperature and rainfall.
8:30 - Overview of the MLP predictor architecture.
10:59 - Training the MLP model with fixed input size.
11:05 - Reviewing training results and prediction accuracy.
13:05 - Exploring prediction rollout with test data.
16:48 - Introduction to sequential training with feedback loop.
18:48 - Training the model with sequential input.
20:02 - Evaluating prediction results with sequential training.
22:34 - Conclusion and preview of upcoming topics.
In this video we start section 12 looking at sequential and time series data with a basic Auto-regressive MLP task.
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The corresponding code is available here! (Section12)
https://github.com/LukeDitria/pytorch...