Geometric deep learning is a “program” that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The priors, such as various types of invariance, first arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present a very conceptual, high-level overview, and highlight a few applications.
In this episode of Open Source Directions, we were joined by Thomas Wiecki once again who talked about the work being done with PyMC. PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Fortran is a compiled language which means that once written, the source code must be passed through a compiler to produce a machine executable that can be run.
We were joined by Joris Van den Bossche talked about the work being done with Pandas. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
In this episode of Open Source Directions, we were joined by Aaron Meurer who will talk once again with Oscar Benjamin about the work he has been doing with SymPy. SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.
In this episode, we have an engaging and very entertaining discussion with Jono Bacon, the founder of Jono Bacon Consulting. Jono was Director of Community at notable companies such as Github, Canonical, and XPRIZE. He is one of the top (if not the top) experts in the world when it comes to building strong communities.
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