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Tag Archives: machine learning
Compressing VGG for Style Transfer
I recently implemented pastiche—discussed in a prior post—for applying neural style transfer. I encountered a size limit when uploading the library to PyPI, as a package cannot exceed 60MB. The 32bit floating point weights for the underlying VGG model [1] … Continue reading
kmeans1d: Globally Optimal Efficient 1D kmeans Clustering
I implemented kmeans1d, a Python library for performing kmeans clustering on 1D data, based on the algorithm in (Xiaolin 1991), as presented in section 2.2 of (Grønlund et al., 2017). Globally optimal kmeans clustering is NPhard for multidimensional data. LLoyd’s … Continue reading
kmeans Image Color Quantization
I implemented a web page that can apply color quantization to images using kmeans clustering. Here’s the link: https://dstein64.github.io/kmeansquantizationjs/ The JavaScript source code is available on GitHub: https://github.com/dstein64/kmeansquantizationjs
Factorization Machines with Theano
A Factorization Machine (FM) is a predictive model that can be used for regression and classification (Rendle 2010). FMs efficiently incorporate pairwise interactions by using factorized parameters. PyFactorizationMachines is a Theanobased Python implementation of factorization machines. Update 4/20/2017: The library is now … Continue reading
Matrix Factorization with Theano
Matrix factorization algorithms factorize a matrix D into two matrices P and Q, such that D ≈ PQ. By limiting the dimensionality of P and Q, PQ provides a lowrank approximation of D. While singular value decomposition (SVD) can also be … Continue reading