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Awesome song and introduction

Importing the modules

An outline of an LSTM class

init: Creating and initializing the tensors

lstm_unit: Doing the LSTM math

forward: Make a forward pass through an unrolled LSTM

configure_optimizers: Configure the...optimizers.

training_step: Calculate the loss and log progress

Using and training our homemade LSTM

Evaluating training with TensorBoard

Adding more epochs to training

Using and training PyTorch's nn.lstm

Description:

Learn how to implement and train Long Short-Term Memory (LSTM) networks using PyTorch and Lightning in this comprehensive 33-minute tutorial. Code an LSTM unit from scratch, then utilize PyTorch's nn.LSTM() function for comparison. Discover Lightning's powerful features, including adding training epochs without restarting and easily visualizing training results. Explore key concepts such as importing modules, creating LSTM classes, initializing tensors, performing LSTM calculations, configuring optimizers, and calculating loss. Gain hands-on experience in training both custom-built and PyTorch-provided LSTM models, and learn to evaluate training progress using TensorBoard. Perfect for those looking to deepen their understanding of LSTM implementation and training techniques in PyTorch and Lightning.

Long Short-Term Memory with PyTorch + Lightning

StatQuest with Josh Starmer

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#Computer Science #Artificial Intelligence #Neural Networks #Recurrent Neural Networks (RNN) #Long short-term memory (LSTM) #Deep Learning #PyTorch #Machine Learning #TensorFlow #Tensorboard