Many fields are benefiting from the use of deep learning, and with the R keras, tensorflow and related packages, you can now easily do state of the art deep learning in R. In this post, we want to give some orientation as to how to best get started.
Table of Contents What Is Keras Core? Configuring Your Development Environment Let’s Talk about Keras! Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup…
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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.
In this episode of Open Source Directions we were joined by Matthew Seal who talked about the work he has been doing with Jupyter and Nteract. Matthew also discussed a particular topic: common Jupyter tools and their adoption for various use cases in the wild.
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.
Jupyter Book lets you build an online book using a collection of Jupyter Notebooks and Markdown files. Its output is similar to the excellent Bookdown tool, and adds extra functionality for people running a Jupyter stack.
