acikmese

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

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software

NVIDIA graphics card driver (v450.57)
CUDA (v11.0.2)
cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

Guide Page

To start using this site you need to have a GitHub account to sign in. Once signed in it will create your profiles information based on your GitHub account and return you to your brand new profile page. Click the profile editor button to enter in if you want to be a student, partner or teacher. You should also enter in what skills you have and what skills you are looking to learn on this page.

Once you have your profile how you like it, head on over to the search page to look for what you want to use on your next project and what kind of partner you are looking for. After hitting the search button we will find the very best matches for you to begin your pair programming journey!

What is Pair Programming?

Amazon Web Services (AWS) & Google Cloud Platform (GCP) explained in simple English

This guide is only representative from my point of view and it may not be accurate and you should go on the official AWS & GCP websites for accurate and detailed information. It's initially inspired by AWS in simple English and GCP for AWS professionals. The idea is to compare both services, give simple one-line explanation and examples with other software that might have similiar capabilities. Comment below for suggestions.

Category	Service	AWS	GCP	Description	It's like
Compute	IaaS	Amazon Elastic Compute Cloud (EC2)	Google Compute Engine	Type-1 virtual servers	VMware ESXi, Citrix XenServer
	PaaS	AWS Elastic Beanstalk	Google App Engine	Running your app on a platform

	import numpy as np
	from keras import backend as K
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation, Flatten
	from keras.layers.convolutional import Convolution2D, MaxPooling2D
	from keras.preprocessing.image import ImageDataGenerator
	from sklearn.metrics import classification_report, confusion_matrix

	#Start
	train_data_path = 'F://data//Train'

	import numpy as np
	import pandas as pd
	from collections import defaultdict
	from scipy.stats import hmean
	from scipy.spatial.distance import cdist
	from scipy import stats
	import numbers


	def weighted_hamming(data):