docker run --rm -it ubuntu:18.10 /bin/bash
docker build -t chyld/ds:1 .
docker images
docker image rm <ID>
docker run --rm -it chyld/ds:1 /bin/bash --login
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Data Science Workflow | |
- Collect Data | |
- Load into Pandas | |
- EDA | |
- Eliminate null values | |
- Filter - remove rows, cols | |
- Impute - replace missing values with mean, median, mode, 0, 1 | |
- Advanced Impute - if missing values are real, use regression with |
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# COLORS | |
c0="\[\e[38;2;0;255;255m\]" | |
c1="\[\e[38;2;220;20;60m\]" | |
c2="\[\e[38;2;255;69;0m\]" | |
c3="\[\e[38;2;255;215;0m\]" | |
c4="\[\e[38;2;173;255;47m\]" | |
c5="\[\e[38;2;46;139;87m\]" | |
c6="\[\e[38;2;0;128;128m\]" | |
c7="\[\e[38;2;0;191;255m\]" | |
c8="\[\e[38;2;138;43;226m\]" |
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Host mint | |
HostName 172.16.237.200 | |
User chyld | |
Host mintr | |
HostName 172.16.237.200 | |
User chyld | |
LocalForward 127.0.0.1:3000 127.0.0.1:3000 | |
LocalForward 127.0.0.1:3001 127.0.0.1:3001 | |
LocalForward 127.0.0.1:3002 127.0.0.1:3002 | |
LocalForward 127.0.0.1:3003 127.0.0.1:3003 |
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name: py38 | |
dependencies: | |
- python=3.8 | |
- pip | |
- pip: | |
- ipython | |
- jupyterlab | |
- notebook | |
- voila | |
- numpy |
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# updated for ubuntu 19.10 | |
sudo add-apt-repository ppa:git-core/ppa | |
sudo add-apt-repository ppa:jonathonf/vim | |
sudo add-apt-repository ppa:lazygit-team/release | |
sudo apt-get update | |
sudo apt-get install pydf ack inotify-tools curl htop tree build-essential neofetch tmux sqlite3 zsh silversearcher-ag hwinfo ranger screen tilix gparted jq openssh-server git vim lua5.3 fzy xclip dstat xsel lazygit colortest | |
sudo apt install snapd | |
sudo snap install bitwise |
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can a nn fit to non linear data | |
when scaling ... minmax or standard | |
what is forward prop | |
what is backward prop | |
what is a loss | |
draw a diagram, show a few weights... ask about partial derivitives | |
what is a batch size | |
what is epoch | |
when to stop training | |
what is an optimizer |
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You want to predict whether your 2011 Toyota Camry will get better than 35 mpg. | |
You collect data about various cars, and run it through a Logistic Regression model. | |
Here are the coefficients 0.00000035, 0.0002, 0.31 related to the features weight, hp, acceleration respectively. | |
When all features are zero, y is zero. | |
Your car, the Camry, weighs 2100 lbs, has 125 hp and has 9.1 acceleration. | |
Let's say your threshold for positive class is 0.95. |
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# ~/.bashrc: executed by bash(1) for non-login shells. | |
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc) | |
# for examples | |
# If not running interactively, don't do anything | |
case $- in | |
*i*) ;; | |
*) return;; | |
esac |
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Pop Quiz: | |
You have a black box that takes in a sequence of numbers as follows: | |
input = 3.1, 54.2, 18.1, -44.9 | |
... and it outputs these numbers. | |
output = 37.58, 129.56, 64.58, -48.82 | |
If you were to input the number 310.5 into this black box: | |
- What would the output be? |