stylegan2-projecting-images VS gophernotes

A Colab notebook.

To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.

Please head over to the Google Colab

But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.

访问 Google Colab 。

Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.

Open your web browser and navigate to Google Colab.

Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.

https://github.com/gopherdata/gophernotes

I've had this bookmarked for some time and just havent gotten around to it.

Gopher Notes Kernel for jupyter notebooks? Could be useful :)

I started this because gophernotes was not working for another project I'm slowing working on -- it is interpreted, and not up-to-date (generics, etc).

A go kernel is available at https://github.com/gopherdata/gophernotes

> You do know that statically typed languages have REPLs too? Like the ML family, including Haskell.

I do, but that I don't see how that relates to the bit of my post which you've quoted. I certainly didn't claim or imply that REPL and static type systems were mutually exclusive, only that REPLs are a poor substitute for many static analysis tasks.

> And when using something like a Jupyter notebook with a kernel for your compiled language https://github.com/gopherdata/gophernotes you can do similar interactive programming.

Yeah, I'm aware. I operate a large JupyterHub cluster (among many other things) at work. :)

> Lisp REPLs take that a step further, as you interact with and in your whole actually running program.

That sounds nice, but it's too abstract to persuade IMHO.

If you are coming from python,jupyter notebook, gophernotes is a great library to setup your own playground.

Why can't you just build libraries to make Go a better language for data science? There's already Go support for a Jupyter Notebooks kernel: https://github.com/gopherdata/gophernotes