Marius Vach vacmar01

Allgemeine Tips

GPU in Google Colab einschalten: "Laufzeit -> Laufzeittyp ändern -> Hardwarebeschleuniger -> T4 GPU"

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How to Install Nginx, MySQL and PHP on an Ubuntu Droplet from DigitalOcean

This is meant to be as a reminder to myself and as a quick and dirty reference to others (heavily inspired by: https://www.techalyst.com/posts/laravel-hosting-with-digital-ocean-droplet-step-by-step-tutorial). It assumes you want to install a Laravel app afterwards, but it should be easily adaptable to other PHP apps.

I know that there are probably much more things you could config in your nginx (and maybe you should), but this works for now for me. I might update this with new things I learn.

1. Create DigitalOcean droplet

Choose the region that is nearest to you.

Super Kurs und super (online kostenlos zu lesendes) Buch. Sehr anschaulich erklärt.

Sehr aufwendig produzierter Kurs vom MIT zum Thema Deep Learning mit mehreren fortgeschrittenen Themen in späteren Videos

http://introtodeeplearning.com/ (MIT Boston)

"Introduction to Deep Learning" Kurs der TU München

https://niessner.github.io/I2DL/ (TU München)

	# /// script
	# dependencies = [
	# "dspy",
	# "rich"
	# ]
	# ///

	import dspy
	import os
	import inspect

	➜ random_experiments uv run agent.py
	Reading inline script metadata from `agent.py`
	Enter your message or type 'exit' to quit.
	👨‍💻User: what is the biggest file in the current folder?
	Reasoning: To find the biggest file in the current folder, I first need to list the files
	in the current directory. Once I have the list, I can inspect each file's size and
	determine which one is the largest. The `list_files` function will give me the names of the
	files, and then I can use `run_cmd` with a command like `ls -l` or `du -h` to get their
	sizes.
	Selected Function: list_files

	from fasthtml.common import (
	fast_app,
	serve,
	Main,
	H1,
	Article,
	Div,
	Form,
	Textarea,
	NotStr,

	from fasthtml.common import *
	from dataclasses import make_dataclass, fields
	import datetime
	from pathlib import Path

	import yaml

	def create_dataclass_from_schema(schema_dict, cls_name="DynamicClass"):
	str2type = {
	"str": str,

	class Interpreter:
	def __init__(self, model, dl):
	self.model = model
	self.dl = dl
	if hasattr(model, "loss"):
	self.loss_func = self.model.loss
	self.losses = torch.empty(0)
	self.model.eval()
	for batch in tqdm.tqdm(self.dl):
	x, y = batch

	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# How to create a validation set for image classification"
	]
	},
	{