Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
| https://github.com/Asabeneh/30-Days-Of-Python |
| import json | |
| import xlwings as xw | |
| import pandas as pd | |
| fileName = 'Sample_Exhibit_01.xlsx' | |
| metadata = "metadata.json" | |
| sample_data = { | |
| "ExhibitName01":{ |
| var express = require('express'), | |
| ntlm = require('express-ntlm'); | |
| const port = 3000; | |
| var app = express(); | |
| app.use(ntlm({ | |
| debug: function() { | |
| var args = Array.prototype.slice.apply(arguments); | |
| console.log.apply(null, args); | |
| }, |
| var express = require('express'), | |
| ntlm = require('express-ntlm'); | |
| const port = 3000; | |
| var app = express(); | |
| app.use(ntlm({ | |
| debug: function() { | |
| var args = Array.prototype.slice.apply(arguments); | |
| console.log.apply(null, args); | |
| }, |
| var express = require('express'), | |
| ntlm = require('express-ntlm'); | |
| var app = express(); | |
| app.use(ntlm()); | |
| app.all('*', function(request, response) { | |
| response.end(JSON.stringify(request.ntlm)); }); | |
| app.listen(9000); |
| from http.server import HTTPServer, BaseHTTPRequestHandler | |
| from io import BytesIO | |
| class SimpleHTTPRequestHandler(BaseHTTPRequestHandler): | |
| def do_GET(self): | |
| self.send_response(200) | |
| self.end_headers() | |
| self.wfile.write(b'Hello, World!') |
| import openpyxl | |
| import pandas as pd | |
| import json | |
| import ast | |
| wb = openpyxl.load_workbook(r'C:\Users\Ausu\Documents\C(Sharp) Projects\Sample_JsonGenMetadata.xltm') | |
| def getSheetNameTable(): | |
| listofSheetName = [] | |
| worksheet = wb["Metadata"] |
| { | |
| "$ref": "#/definitions/Welcome", | |
| "definitions": { | |
| "Welcome": { | |
| "type": "object", | |
| "additionalProperties": false, | |
| "properties": { | |
| "version": { | |
| "type": "integer" | |
| }, |