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thedarkside.frantzmiccoli.com | ||
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teddykoker.com
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| | | | | Gradient-descent-based optimizers have long been used as the optimization algorithm of choice for deep learning models. Over the years, various modifications to the basic mini-batch gradient descent have been proposed, such as adding momentum or Nesterovs Accelerated Gradient (Sutskever et al., 2013), as well as the popular Adam optimizer (Kingma & Ba, 2014). The paper Learning to Learn by Gradient Descent by Gradient Descent (Andrychowicz et al., 2016) demonstrates how the optimizer itself can be replac... | |
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datadan.io
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| | | | | Linear regression and gradient descent are techniques that form the basis of many other, more complicated, ML/AI techniques (e.g., deep learning models). They are, thus, building blocks that all ML/AI engineers need to understand. | |
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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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a16z.com
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| | | A curated list of resources we've relied on to get smarter about modern AI, including generative AI, LLMs, and transformer models. | ||
