In this StatQuest we'll learn how to code an LSTM unit from scratch and then train it. Then we'll do the same thing with the PyTorch function nn.LSMT(). Along the way we'll learn two cool tricks that Lightning gives us that make our lives easier: 1) How to add more training epochs without starting over and 2) How to easily visualize the training results to determine if you need to do more training or are done.
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0:00 Awesome song and introduction
4:25 Importing the modules
5:39 An outline of an LSTM class
6:56 init(): Creating and initializing the tensors
9:09 lstm_unit(): Doing the LSTM math
12:25 forward(): Make a forward pass through an unrolled LSTM
13:42 configure_optimizers(): Configure the...optimizers.
14:00 training_step(): Calculate the loss and log progress
16:40 Using and training our homemade LSTM
20:43 Evaluating training with TensorBoard
23:22 Adding more epochs to training
26:18 Using and training PyTorch's nn.lstm()
#StatQuest
22 июн 2024
