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www.analyticsvidhya.com | ||
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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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vxlabs.com
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| | | | | I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. | |
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blog.keras.io
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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. | |
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sirupsen.com
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| | | [AI summary] The article provides an in-depth explanation of how to build a neural network from scratch, focusing on the implementation of a simple average function and the introduction of activation functions for non-linear tasks. It discusses the use of matrix operations, the importance of GPUs for acceleration, and the role of activation functions like ReLU. The author also outlines next steps for further exploration, such as expanding the model, adding layers, and training on datasets like MNIST. | ||
