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 teddykoker.com
Learning to Learn with JAX | Teddy Koker
| | bdtechtalks.com
A simple guide to gradient descent in machine learning - TechTalks
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| | Gradient descent is the main technique for training machine learning and deep learning models. Read all about it.
| | questionableengineering.com
Numpy LeNet 5 with ADAM | Questionable Engineering
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| | John W Grun AbstractIn this paper, a manually implemented LeNet-5 convolutional NN with an Adam optimizer written in Numpy will be presented. This paper will also cover a description of the data use
| | blog.evjang.com
Eric Jang: Meta-Learning in 50 Lines of JAX
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| | Github repo here: https://github.com/ericjang/maml-jax Adaptive behavior in humans and animals occurs at many time scales: when I use a n...
| | blog.fastforwardlabs.com
"Hello world" in Keras (or, Scikit-learn versus Keras)
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| This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.