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Workplace profile photo guidelines

Criteria for workplace profile photo:

• Simple - Only you, no one else. Should not have a busy background or outfit.
• The photo should be clear, in focus, and with good contrast.
• You should look friendly and directly at the camera. You should be square to the camera.
• Your face should take up a majority of the frame. Your entire face should be visible. Your hands should not be visible.
• Should look like the best version of yourself on a daily basis.

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Brian Spiering's Writing Tips

1. Write down all ideas. You don't need to develop all ideas.
2. Start by editing previous work. It sets the context.
3. Write, write, write. Bad first drafts done quickly are the best drafts.
4. Quit when you start making mistakes.

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# I shouldn't install packages into `base` env. But I do. Here they are:
name: base
channels:
  - pytorch
  - conda-forge
  - defaults
dependencies:
  - _ipyw_jlab_nb_ext_conf=0.1.0=py36_0
  - _r-mutex=1.0.0=anacondar_1
  - _tflow_select=2.3.0=mkl

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Know

1. Define in your own words.
2. Construct an analogy.
3. Identify a practical example.
4. Define in technical terms and mathematically.

DO

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Brian Spiering's Data Science Principles

1. All the data, all the time.

2. Data is its own best model.

3. End-to-end solution, first. Then iterate.

4. Simple working system then add complexity (if needed).

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# Setup Packages ---------------------------------------------------------------
# List of packages for session
.packages <- c("devtools", "dplyr", "ggplot2", "markdown")

# Install CRAN packages (if not already installed)
.inst <- .packages %in% installed.packages()
if(length(.packages[!.inst]) > 0) install.packages(.packages[!.inst])

# Load packages for session 
lapply(.packages, require, character.only=TRUE)

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#! /usr/bin/env Rscript
 
# File description -------------------------------------------------------------
# 
#
 
# Setup packages ---------------------------------------------------------------
# List of packages for session
.packages = c("devtools", 
    "stringr", 

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A Hacker's Guide to Python string and Natural Language Processing (NLP) packages

Extraction

• textract - Extract text from any document.
• camelot - Extract text from PDF.

Preprocessing