Recurrent Neural Network, Serverless with Webassembly and S3

During the past weeks, I’ve had the opportunity to play a bit with Wasm and Go. All those experiments led me to a write a proof of concepts that can illustrate everything I have said recently about: Thinking the deep-learning stack like an Ops (see my post about NNRE/NNDK). Capturing the real value of the training process (the knowledge) into a sequence of bits (the lightning talk I gave about it at the dotAI should be online soon).

Some notes about the upcoming WebAssembly support in Go

This is a rapid post about webassembly. Its goal is to act as a reminder for me more than a tutorial on how to use it. The upcoming version of go 1.11 will have support for Wasm. @neelance has done most of the work of implementation. The support for wasm can already be tested by extracting his working branch from GitHub. See this article for more information. Setup of the toolchain To generate a wasm file from a go source, you need to get and patch the go toolset from the sources:

Considerations about software 2.0

Disclaimer This is a technical article about a work in progress. The primary goal is to document what I did and to clarify my ideas. A more general and complete article about software 2.0 is in development and should be published on my company’s blog later. This post describes the concept of software 2.0. It evaluates an instance of the Unicode equation parser (as described here) to give a strict separation of the software 1.

Parsing mathematical equation to generate computation graphs - First step from software 1.0 to 2.0 in go

In a previous article, I described an implementation of an RNN from scratch in go. The target is to use the RNN as a processing unit. The ultimate goal is to create a portable tool cross platform and able to grab and process data where they are. I have many applications in mind such as finding the root-cause of an incident or managing the capacity of an infrastructure. Note I stick to the Go language for many reasons: Some of them a personnal and not opposable (I simply like it).

About Recurrent Neural Network, Shakespeare and GO

Shakespeare and I, encounter of the third type A couple of months ago, I attended the Google Cloud Next 17 event in London. Among the talks about SRE, and keynotes, I had the chance to listen to Martin Gorner’s excellent introduction: TensorFlow and Deep Learning without a PhD, Part 2. If you don’t want to look at the video, here is a quick summary: a 100 of lines of python are reading all Shakespeare’s plays; it learns his style, and then generates a brand new play from scratch.