8bit-pixies

write a glowing review for my real estate agent who sold my apartment and provided attention to our particular requests and wants

Subject: A Stellar Real Estate Experience with [Agent's Name]

I recently had the pleasure of working with [Agent's Name] from [Real Estate Agency] to sell my apartment, and I cannot express enough how impressed I am with the level of service and professionalism I received throughout the entire process.

From our initial consultation to the successful closing, [Agent's Name] demonstrated a deep understanding of the real estate market and a genuine commitment to helping me achieve the best possible outcome. Their expertise and guidance were invaluable, making what could have been a stressful experience remarkably smooth and efficient.

One of the standout qualities of [Agent's Name] was their excellent communication skills. They kept me informed every step of the way, promptly responding to my inquiries and providing regular updates on the progress of the sale. This level of tran

8bit-pixies

[Background]
Color=39,40,34

[BackgroundIntense]
Color=65,67,57

[Foreground]
Color=225,225,218

[ForegroundIntense]

8bit-pixies

# The goal of this is to try to make use of Shap to explain a tree built in river.
# https://github.com/online-ml/river/issues/437

from functools import reduce
import operator

import numpy as np
import pandas as pd
import pprint
from sklearn import datasets

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#!/usr/bin/env python
"""
Simple example of a full screen application with a vertical split.

This will show a window on the left for user input. When the user types, the
reversed input is shown on the right. Pressing Ctrl-Q will quit the application.
"""
from prompt_toolkit.application import Application
from prompt_toolkit.buffer import Buffer
from prompt_toolkit.key_binding import KeyBindings

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import pandas as pd
import statsmodels.api as sm


def test_rolling_mode():
    """
    The goal of this application level monitoring is to react in light of changes
    to re-model fits and movement in the modelling process. In the linear model
    scenario, confidence intervals around the coefficients can be provided which
    can be used as a measure for model drift over time.

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"""
This is a manual implementaiton of grucell so that it will work in more
general envrionments...
"""

import tensorflow as tf


input_size = 64
cell_size = 32

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<script src='https://unpkg.com/tesseract.js@v2.0.0-alpha.3/dist/tesseract.min.js'></script>
<input type="file" onchange="getRego(this.files)">

<script>
function getRego(files) {
    const worker = new Tesseract.TesseractWorker();
    worker.recognize(files[0]).then(function(data){
        // construct something which glues all the words together...
        console.log(data.text);
        })

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import numpy as np

def linear_part(x, w):
    return x * w

def non_linear(x, p=0.001):
    multi = (x > 0).astype(np.float64)
    multi[multi == 0] = 0.001
    return x * multi

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pareto_principle <- function(x){
  return (x ^ (log(0.2)/log(0.8)))
}

sprintf("%.2f of the effort comes from %.2f of the causes", 0.99, pareto_principle(0.99))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.95, pareto_principle(0.95))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.8, pareto_principle(0.8))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.5, pareto_principle(0.5))

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FROM jupyter/r-notebook

# docker build --rm -t jupyter/extended-notebook .
# docker run --rm -it -p 8888:8888 jupyter/extended-notebook


RUN conda install --quiet --yes -c r rstudio=1.1*
RUN conda install --quiet --yes -c anaconda pandas scipy gensim scikit-learn

# install nbrsessionproxy