philschmid

Classify user search queries as either "Good Google Search Query" or "Bad Google Search Query" based on their likelihood of yielding relevant and helpful results from Google Search.

Input: User search query (text string).

Output: Classification label:
    * Good Google Search Query: The query is likely to be effectively answered by Google Search.
    * Bad Google Search Query: The query is unlikely to be effectively answered by Google Search.  Further categorize "Bad" queries into subtypes for better understanding and classifier training (optional but highly recommended):
        * Chit-Chat/Conversational/Social
        * Personal/Subjective/Opinion-Based (Un-searchable)
        * Vague/Ambiguous/Lacking Specificity

jpbarto

#!/bin/bash

set -e

sudo -u ec2-user -i <<'EOP'
## INSTALL THEIA IDE FROM SOURCE
EC2_HOME=/home/ec2-user
mkdir ${EC2_HOME}/theia && cd ${EC2_HOME}/theia
### begin by installing NVM, NodeJS v10, and Yarn
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.5/install.sh | bash

kukuruza

from math import sqrt

def put_kernels_on_grid (kernel, pad = 1):

  '''Visualize conv. filters as an image (mostly for the 1st layer).
  Arranges filters into a grid, with some paddings between adjacent filters.

  Args:
    kernel:            tensor of shape [Y, X, NumChannels, NumKernels]
    pad:               number of black pixels around each filter (between them)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

kevin-smets

Default

Powerlevel10k