smrati

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

Multiple SSH Keys settings for different github account

create different public key

create different ssh key according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "your_email@youremail.com"

	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'

	#!/bin/bash

	### USAGE
	###
	### ./ElasticSearch.sh 1.7 will install Elasticsearch 1.7
	### ./ElasticSearch.sh will fail because no version was specified (exit code 1)
	###
	### CLI options Contributed by @janpieper
	### Check http://www.elasticsearch.org/download/ for latest version of ElasticSearch

	from flask import Flask
	from flask import request
	app = Flask(__name__)

	#Normally this would be an external file like object, but here
	#it's inlined
	FORM_PAGE = """
	<html>
	<head>
	<title>Flask Form</title>

	from PIL import Image
	from PIL.ExifTags import TAGS, GPSTAGS

	def get_exif_data(image):
	"""Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
	exif_data = {}
	info = image._getexif()
	if info:
	for tag, value in info.items():
	decoded = TAGS.get(tag, tag)