thomasnield

import pandas as pd
import torch

"""
Complete the code below to perform stochastic gradient descent
on a linear regression.

Try to find a sufficient learning rate and number of iterations. 
"""

thomasnield

import pandas as pd
import torch

"""
COMPLETE THE CODE BELOW BY REPLACING THE QUESTION MARKS ?'s
SO FORWARD PROPAGATION IS COMPLETE 
"""


url = "https://raw.githubusercontent.com/thomasnield/machine-learning-demo-data/master/classification/maintenance_predict.csv"

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"""
Neural network prediction on a maintenance dataset using PyTorch.
Predicts whether a part needs replacement (1) or not (0).
Uses 3 nodes in the hidden layer and ReLU as the activation function.
"""

import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset

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from random import randint, choice

def random_door():
    return randint(1, 3)

trial_count = 10_000

stay_wins = 0
switch_wins = 0

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use std::process::Command;
use walkdir::WalkDir;
use regex::Regex;

/*
[package]
name = "rust_playground"
version = "0.1.0"
edition = "2021"

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cd ~/git/my_project_with_large_files
git lfs install
git lfs migrate import --include="*.pptx, *.pdf" --everything
git lfs ls-files
git push --all --force
# https://josh-ops.com/posts/migrate-to-git-lfs/

thomasnield

from manim import *
import os 

class CircleTraceTest(Scene):
    def construct(self):
        circle = Circle(radius=2)
        self.add(circle)

        vt = ValueTracker(0.0)
        point: Dot = always_redraw(lambda: Dot(point=circle.point_from_proportion(vt.get_value())))

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# import required module
import os
from pathlib import Path
import re

# assign directory
directory = Path('/Users') / 'thomasnield' / 'git' / 'oreilly_essential_math_for_data_science_book'

def two_digit_correct(filename: str):
    return re.sub(r"(?<=[0-9])_(?=[0-9][A-z])", "_0", filename)

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%%manim -qk Histogram 

"""
PROMPT: Create sample Python code using manim to plot data into a histogram. Use that same data to create a numberline with points beneath it. Then draw a distribution function over the histogram. 
"""
from manim import *
import numpy as np

class Histogram(Scene):
    def construct(self):

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[
     {
            "ID" : 1,
            "NAME": "Alpha Medical",
            "ADDRESS" : "18745 Train Dr",
            "CITY" : "Dallas",
            "STATE": "TX",
            "ZIP" : 75021,
            "CATEGORY" : "INDUSTRIAL"
        },