dan-palmer

{
  "messages": [
    {
      "content": "You are an advanced, reliable, candid AI system that takes user search queries, converts them into questions, and answers them, using specific facts and details sourced from webpages to prove your answer. You admit when you're unsure or don't know, and you never make a statement without providing a fact or instance to back it up. You answer questions directly and clearly, then provide more detail later. You follow the JSON schema exactly.",
      "role": "system"
    },
    {
      "content": "# CONTEXT\nCurrent date: #{DATE_TIME}.\n\nHere are result  from a web search for '#{QUERY}':\nBEGIN WEB PAGE #{HOST_1} #{MARKDOWN_1}END WEB PAGE\nBEGIN WEB PAGE #{HOST_2} #{MARKDOWN_2}END WEB PAGE\nBEGIN WEB PAGE #{HOST_3} #{MARKDOWN_3}END WEB PAGE\nBEGIN WEB PAGE #{HOST_4} #{MARKDOWN_4}END WEB PAGE\nBEGIN WEB PAGE #{HOST_5} #{MARKDOWN_5}END WEB PAGE\nBEGIN WEB PAGE #{HOST_6} #{MARKDOWN_6}END WEB PAGE",
      "role": "system"
    },

prescience-data

import { IncomingMessage, RequestListener, ServerResponse } from "http"
import { createServer, Server } from "https"
import puppeteer, {
  Browser,
  BrowserLaunchArgumentOptions,
  Protocol
} from "puppeteer-core"

import { Page } from "./types"
import Cookie = Protocol.Network.Cookie

tayvano

jinyu121

# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import numpy as np
import os
import random

kevvzub

import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np

# Loading datasets'

thmavri

#determine the labels
import pyvw #vw python interface

DEST  = 1
PROP = 2
FAC = 3

...
#create the class for the Sequence Labeler
class SequenceLabeler(pyvw.SearchTask):

leonardofed

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

---

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

nderkach

#!/usr/bin/env python
#
# Simple script showing how to read a mitmproxy dump file
#

### UPD: this feature is now avaiable in mitmproxy: https://github.com/mitmproxy/mitmproxy/pull/619

from libmproxy import flow
import json, sys