“People think that just because IT doesn’t use motor oil and carry physical packages that it doesn’t need preventive maintenance,” Erik says, chuckling to himself. “That somehow, because the work and the cargo that IT carries are invisible,
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“you can also play with the LSTM. We’ve used a simple approach, but you can increase num_layers to create stacked LSTMs, increase or decrease the number of hidden features in the layer, or set bidirectional=true to create a biLSTM. Replacing the entire LSTM with a GRU layer would also be an interesting thing to try; does it train faster? Is it more accurate? Experiment and see what you find!”
― Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
― Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
Hasan Basri’s 2024 Year in Books
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