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saturncloud.io | ||
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www.paepper.com
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| | | | | When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient. | |
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www.jeremymorgan.com
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| | | | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | |
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wtfleming.github.io
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| | | | | [AI summary] This post discusses achieving 99.1% accuracy in binary image classification of cats and dogs using an ensemble of ResNet models with PyTorch. | |
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www.analyticsvidhya.com
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| | | Explore RNNs: their unique architecture, working principles, BPTT, pros/cons, and Python implementation using Keras. | ||
