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zserge.com | ||
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vankessel.io
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| | | | | A blog for my thoughts. Mostly philosophy, math, and programming. | |
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www.paepper.com
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| | | | | [AI summary] This article explains how to train a simple neural network using Numpy in Python without relying on frameworks like TensorFlow or PyTorch, focusing on the implementation of ReLU activation, weight initialization, and gradient descent for optimization. | |
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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. | |
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yang-song.net
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| | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | ||
