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Nerfstudio (with COLMAP* + HLOC* + FFMPEG*) on Windows via WSL2

* Via Pixi

Old instructions stopped working on Gsplat 1.0.0+. Updated and simplified on 2024-07-09. Now no longer deviates from official installation instructions.

2024-11-03: Microsoft updated the default WSL2 distro to Ubuntu 24.04. One extra command is needed to make it work now.

For native Linux start-to-finish installation instructions, see this document instead.

For context: WSL2 is a Windows built-in way to run Linux applications on Windows, in a tightly integrated virtual machine.
This, in theory, lets you run Linux applications without the complexity and trouble of actually having to run Linux.
In practice, there are still a few caveats, most notably the complex custom CUDA implementation Windows uses to make this work.

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You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

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Autonomous Systems Interview Preparations

This document contains some interview questions as provided in Udacity's Robotics Engineer Nanodegree program.

Project Instructions

Role Selection

It's time for you to practice your interviewing skills! Over the next several pages, you'll see that we have specific roles in Autonomous Systems, just like the videos you watched before:

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Panels:
  - Class: rviz_common/Displays
    Help Height: 78
    Name: Displays
    Property Tree Widget:
      Expanded:
        - /Global Options1
        - /Status1
      Splitter Ratio: 0.5
    Tree Height: 190

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<?xml version="1.0"?>
<robot name="sam_bot"
  xmlns:xacro="http://ros.org/wiki/xacro">

  <!-- Define robot constants -->
  <xacro:property name="base_width" value="0.31"/>
  <xacro:property name="base_length" value="0.42"/>
  <xacro:property name="base_height" value="0.18"/>

  <xacro:property name="wheel_radius" value="0.10"/>

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import launch
from launch.actions import (
    ExecuteProcess,
    DeclareLaunchArgument,
    RegisterEventHandler,
    SetEnvironmentVariable,
)
from launch.conditions import IfCondition
from launch.event_handlers import OnProcessExit
from launch.substitutions import (

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Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

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ML Learning material:

• ULTIMATE RESOURCE 🐝 ---> https://deep-learning-drizzle.github.io/

• SDC course:

  • CS4804 Intro to AI, Fall 2016: https://youtube.com/playlist?list=PLUenpfvlyoa1iiSbGy9BBewgiXjzxVgBd

• Probability:

  • Khan Academy: https://youtube.com/playlist?list=PLC58778F28211FA19

• Mathematical Monk: https://youtube.com/playlist?list=PL17567A1A3F5DB5E4

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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F

class FasterRCNN(nn.Module):
    def __init__(self, num_classes):
        super(FasterRCNN, self).__init__()