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 github.com
GitHub - tensorflow/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessibl...
| | www.altexsoft.com
Machine Learning Pipeline: Architecture of ML Platform
3.8 parsecs away

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| | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment.
| | www.ntentional.com
ICLR 2020: Efficient Deep Learning and More | ntentional
3.5 parsecs away

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| | Highlights from my favorite Deep Learning efficiency-related papers at ICLR 2020
| | www.jeremymorgan.com
How to Get Started With PyTorch
6.0 parsecs away

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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.
| | blog.keras.io
Building Autoencoders in Keras
15.0 parsecs away

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| [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics.