Machine Learning
Fighting Archetypal Overfitting of Data
Overfitting is common-place in machine learning. But, in confining ourselves to graphical data explorations, we are creating a risk for another form of overfitting, notably archetypal overfitting. In this post, I outline why this is a problem, and how to reduce overfitting through better data analysis.
ReadRust for Machine Learning: SIMD, BLAS, and Lapack
Using Rust for machine learning still has a ways to go. It's possible to overcome some of the limitations, however, by getting familiar with the lower-level libraries that drive high-performance linear algebra computing, namely, SIMD, BLAS, and Lapack.
ReadNeural Cookies
Generative adversarial neural networks (GANs) are all the rage. I explore if we can generate cookie recipes with GAN architectures, doing so in a way that's neither NLP nor computer vision.
ReadPersonalizing Campaigns Though Machine Learning
Machine learning, the field upon which the vast majority of artificial intelligence systems depend on, has tremendous potential to do good if harnessed correctly. When used properly, algorithms can allow for better timed phone calls, and conversations directly related to a voter's interests, and hopefully, less robocalls in the middle of dinner.
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