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.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.OEmVVHRkcbqLvFJEofgZvIIihQ5-MMIbh8GyUOoOXSw)
import keras | |
from keras.datasets import mnist | |
import numpy as np | |
from PIL import Image, ImageOps | |
import os | |
def save_image(filename, data_array): | |
im = Image.fromarray(data_array.astype('uint8')) | |
im_invert = ImageOps.invert(im) |
import mmh3 | |
import pandas as pd | |
def get_positive_hash(x): | |
s = " ".join(get_unique_tokens(x)) | |
return mmh3.hash(s) % 2**31 | |
df['group_id'] = df['query_string'].apply(get_positive_hash ) | |
query_groups = df.groupby("group_id") |
import functools | |
import matplotlib.pyplot as plt | |
import matplotlib.patches as mpatches | |
import matplotlib.table as table | |
import numpy as np | |
import pandas as pd | |
from scipy.stats import spearmanr | |
TOURNAMENT_NAME = "kazutsugi" |