http://chrislaing.net/categories/odds-and-ends/ Recent content in odds and ends on chrislaing.net Hugo -- gohugo.io en-gb Wed, 22 Jan 2020 00:00:00 +0000 http://chrislaing.net/blog/odds-and-ends-4/ Wed, 22 Jan 2020 00:00:00 +0000 http://chrislaing.net/blog/odds-and-ends-4/ I just discovered kops, apparently an excellent way to deploy and manage Kubernetes clusters on the cloud. Since managing a k8s cluster seems to be a full-time job at the best of times, I’m interested to see if this really does make it much easier. I have a very mild case of podcast addiction, and am always looking for something great to add to my collection of subscriptions. Nikita Voloboev has published a very interesting list of podcasts. http://chrislaing.net/blog/odds-and-ends-3/ Sun, 02 Jun 2019 00:00:00 +0000 http://chrislaing.net/blog/odds-and-ends-3/ My former colleagues at Blue Yonder (now part of JDA) have introduced Kartothek, software for managing tables stored as parquet files. A great set of documentation on going zero to JupyterHub with Kubernetes. A related piece from Jim Crist on installing JupyterHub on an existing Hadoop cluster. An interesting paper interoducing PATE from a couple of years ago that had passed me by. PATE stands for Private Aggregation of Teacher Ensembles, and is a method for doing semi-supervised transfer learning from private data. http://chrislaing.net/blog/odds-and-ends-2/ Fri, 01 Mar 2019 00:00:00 +0000 http://chrislaing.net/blog/odds-and-ends-2/ Naftali Tishby on the Information Theory of Deep Learning (embedded below). I’m very enthusiastic about this kind of work, and have resolved to find and read more of it. a16z’s AI Playbook may be a couple of years old now, but it’s still an important read. Traces is a Python library for unevenly-spaced time series analysis. I haven’t had a problem to really try this out on, but the website looks slick 👌🏻. http://chrislaing.net/blog/odds-and-ends-1/ Sat, 23 Feb 2019 00:00:00 +0000 http://chrislaing.net/blog/odds-and-ends-1/ Spektral is a framework for relational representation learning, built in Python and based on the Keras API. The field of Geometric Deep Learning is starting to get some traction. In particular, doing Deep Learning on graphs presents some interesting possibilities. RPC Frameworks: gRPC vs Thrift vs RPyC for python. The state of Python Packaging. Packing in Python is both improving dramatically and harder than it should be.