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class Node:
    def __init__(self, val):
        self.val = val
        self.parent = None
        self.left = None
        self.right = None

a = Node(12)
b = Node(6)
c = Node(5)

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1. The author assumes that the C-note is currently only jazz club (proposed) under construction or proposed.

2. The author is also assuming that people in Monroe are unwilling to drive 65 miles to go to the "nearest" jazz club.

3. The author is assuming that the majority of these 100,000 people are people who actually live in Monroe.

4. The author is assuming that people attend the jazz festival because they're jazz fans.

5. The author is assuming that this annual jazz festival is gonna be popular every year subsequently.

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regularization

Any modification we make to a learning algorithm that is intended to reduce its generalization error but not its training error

To some extent, we are trying to fit a square peg (the data-generating process) into a round hole (our model family)

Strategies to create such a large, deep regularized model

Parameter Norm Penalties

L-squared Parameter Regularization

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FROM ubuntu:latest
LABEL maintainer="ehzawad@gmail.com"
ARG DEBIAN_FRONTEND=noninteractive
# ARG VCS_REF


# Avoid "delaying package configuration, since apt-utils is not installed"
RUN apt update && apt install --yes apt-utils

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#include <stdio.h>
#include <stdlib.h>

int main(void)
{
    int is_ok = EXIT_FAILURE;
    const char* fname = "/tmp/unique_name.txt"; // or tmpnam(NULL);
    FILE* fp = fopen(fname, "w+");
    if(!fp) {
        perror("File opening failed");

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Group 31

1. crescendo - peak

2. apprise - inform or tell (someone)

3. extenuating - (of a factor or situation) serving to lesson the seriousness of an offense

4. shirk - avoid or neglect (a duty or responsibility)

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HASKELL PROGRAMMING FROM FIRST PRINCIPLES

CHAPTER1. ALL YOU NEED IS LAMBDA

1.1 All You Need is Lambda

lambda calculus:

computation model, 1930s, Alonzo Church, formalizing a method, Turing machines

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