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Recent items include:

  • A Gentle Introduction to Autoencoders & Latent Space
  • Deep Convolutional Autoencoder for Cryptocurrency Market Analysis!
  • Understanding Transformers (Part 5): The final layers doing some heavy lifting

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towardsdatascience.com /1 day ago

A Gentle Introduction to Autoencoders & Latent Space

Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of...

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supplychaingamechanger.com /3 weeks ago

Deep Convolutional Autoencoder for Cryptocurrency Market Analysis!

Cryptocurrencies have gained immense popularity in recent years, with Bitcoin and Ethereum leading the way. As these digital assets become more mainstream, the need for advanced an...

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medium.com /5 days ago

Understanding Transformers (Part 5): The final layers doing some heavy lifting

LayerNorm, residuals, feed-forward blocks, and the encoder-decoder pipeContinue reading on Data Science Collective »

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dzone.com /1 month ago

Multi-Scale Feature Learning in CNN and U-Net Architectures

Scale variation is a persistent source of error in vision models. A semantic concept can occupy a handful of pixels or most of the frame, and dense prediction tasks such as semanti...

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journals.plos.org /3 weeks ago

pyhgf: A neural network library for predictive coding

by Nicolas Legrand, Lilian Weber, Peter Thestrup Waade, Anna Hedvig Møller Daugaard, Mojtaba Khodadadi, Nace Mikuš, Christoph Mathys Bayesian models of cognition have gained consi...

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towardsdeeplearning.com /1 month ago

Gemma 4 12 b: Google Released The Model Without Encoder And That was My WTF Moment

Gemma 4 12B is an open, encoder-free multimodal model that runs on a 16GB laptop. Here is what that actually means, and why it matters.Continue reading on Towards Deep Learning »

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medium.com

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supplychaingamechanger.com

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towardsdatascience.com

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