# Perfectly Awesome

## All posts, sorted by date (oldest first)

Data Science Interview Questions (2019)
categories:
tags: data-science  interviewing
date: 01 Oct 2019
slug:ds-interview-questions

categories:
tags: machine-learning  paperspace
date: 24 Feb 2020
slug:jupyter

Happy Meals - the Ultimate Product Idea
categories:
tags: ideas  prodmgmt
date: 25 Feb 2020
slug:happy-meals

Meteor 1.9 Release
categories:
tags: javascript  meteorjs  webdev
date: 25 Feb 2020
slug:meteor-1point9-release

categories:
tags: prodmgmt  social-media  twitter
date: 25 Feb 2020

How Tracking Pixels Work (Julia Evans)
categories:
tags: analytics  prodmgmt  webdev
date: 25 Feb 2020
slug:tracking-pixels

UI/UX articles - Feb2020
categories:
tags: uiux
date: 25 Feb 2020
slug:uiux

Compelling Speech Techniques
categories:
tags: influence  persuasion  speaking
date: 25 Feb 2020
slug:speech

The Great CEO Within (Gdoc)
categories:
tags: behavior  leadership  prodmgmt
date: 25 Feb 2020
slug:great-ceo-within

Game & Auction Theory Articles
categories:
tags: auctions  game-theory
date: 11 Mar 2020

Product Market Fit - 10 Ways to Find It
categories:
tags: prodmgmt
date: 15 Mar 2020
slug:product-market-fit

More Data Science Interview Questions
categories:
tags: data-science  interviewing
date: 16 Mar 2020
slug:data-science-interview-questions
the null hypothesis cannot be rejected.
* A P-value <0.05 denotes strong evidence against the null hypothesis --> the null hypothesis can be rejected.
* A P-value =0.05 is the marginal value, indicating it is possible to go either way.
What is an ROC curve? What is AUC?

* A ROC curve = the false positive rate of a model plotted against its true positive rate.
* A completely random prediction will be a straight diagonal. The optimal model will be as close to the axes as possible.
* AUC (Area Under Curve) = a measure how close the ROC curve is to the axes. Higher AUC indicates a higher accuracy.
What is PCA?

* Principal Component Analysis, is a method of dimension reduction - finds n orthogonal vectors that represent the most variance in the data, where n is the dimensions the user wants the data reduced to.
* PCA can speed up jobs or can be used to visualize high-dimensional data.
Explain the bias-variance tradeoff

* Bias is a model error due to an oversimplified ML algorithm -- which can lead to underfitting. * When you train your model at that time model makes simplified assumptions to make the target function easier to understand.
* Low-bias algos: decision trees, KNN, and SVM.
* High-bias algos: linear and logistic regression.
* Variance is a model due an overly complex ML algorithm -- the model learns noise from the training data set, hence performing badly on test data. It can lead to high sensitivity and overfitting.
* Normally, as you increase the complexity of your model, you will see a reduction in error due to lower bias in the model. However, this only happens until a particular point — as you continue to make your model more complex, you end up over-fitting your model.
Why is Softmax often the last operation in a neural network?

* Because it accepts a vector of real numbers and returns a probability distribution. Each element is non-negative and the sum over all components is 1.
What is TF/IDF vectorization?

* Term frequency-inverse document frequency reflects how important a word is to a document in a corpus. It is used as a weighting factor in information retrieval and text mining.
* TF–IDF increases proportionally to the number of times a word appears in the document but decreases proportionally by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.
Compare different types of selection biases

* Sampling bias is a systematic error due to a non-random sampling of a population.
* This causes some members of the population to be less included than others, such as low-income families being excluded from an online poll.
* Time interval bias is when a trial may be terminated early at an extreme value (usually for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
* Data bias is when specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to a previously stated or generally agreed on criteria. * Attrition bias is caused by loss of participants discounting trial subjects that did not run to completion.
Define Error Rate, Accuracy, Sensitivity/Recall, Specificity, Precision, and F-Score.

Where T is True, F is False, P is Positive, and N is Negative, each denoting the number of items in a confusion matrix.
* Error Rate: (FP + FN) / (P + N)
* Accuracy: (TP + TN) / (P + N)
* Sensitivity/Recall: TP / P
* Specificity: TN / N
* Precision: TP / (TP + FP)
* F-Score: Harmonic mean of precision and recall.
Compare correlation and covariance

* Correlation measures & estimates the relationship between two variables, and measures how strongly two variables are related.
* Covariance measures the extent to which two random variables change in tandem.
Why is A/B testing effective?

* A/B testing is hypothesis testing for a randomized experiment with two variables A and B.
* It is effective because it minimizes conscious bias — those in group A do not know that they are in group A, or that there even is a group B, and vice versa.
* However, A/B testing is difficult to perform on any context other than Internet businesses.
Random Numbers: How would you generate a random number between 1 and 7 with only one die?

* One solution is to roll the die twice. This means there are 6 x 6 = 36 possible outcomes. By excluding one combination (say, 6 and 6), there are 35 possible outcomes.
* Therefore if we assign five combinations of rolls (order does matter!) to one number, we can generate a random number between 1 and 7.
* For instance, say we roll a (1, 2). Since we have (hypothetically) defined the roll combinations (1, 1), (1, 2), (1, 3), (1, 4), and (1, 5) to the number 1, the randomly generated number would be 1.
Compare univariate, bivariate, and multivariate analaysis.

* Univariate analyses are performed on only one variable. Examples: pie charts, distribution plots, and boxplots.
* Bivariate analysis map relationships between two variables. Examples: scatterplots or contour plots, as well as time series forecasting.
* Multivariate analysis deals with more than two variables to understand the effect of those variable on a target variable. This can include training neural networks for predictions or SHAP values/permutation importance to find the most important feature. It could also include scatterplots with a third feature like color or size.
What is cross-validation?

* Cross validation measure how well a model generalizes to an entire dataset. A traditional train-test-split method, in which part of the data is randomly selected to be training data and the other fraction test data, may mean that the model performs well on certain randomly selected fractions of test data and poorly on other randomly selected test data.
* In other words, the performance is not nearly indicative of the model’s performance as it is of the randomness of the test data.
* Cross validation splits the data into n segments. The model is trained on n-1 segments of the data and is tested on the remaining segment of data. Then, the model is refreshed and trained on a different set of n-1 segments of data. This repeats until the model has predicted values for the entire data (of which the results are averaged).
What does the ‘naive’ in ‘Naive Bayes’ mean?

* Naive Bayes is based on Bayes’ Theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event. It is considered to be ‘naive’ because it makes assumptions that may or may not be correct. This is why it can be very powerful when used correctly — it can bypass knowledge other models must find because it assumes that it is true.
What are the different kernels in SVM?

Linear Kernel
Polynomial Kernel
Sigmoid Kernel
Recommenders: Compare collaborative filtering, content filtering, and hybrid filtering.

* Collaborative filtering solely relies on user ratings to determine what a new user might like next. All product attributes are either learned through user interactions or discarded. One example of collaborative filtering is matrix factorization.

* Content filtering relies only on intrinsic attributes of products and customers, such as product price, customer age, etc., to make recommendations. One way to achieve content filtering is to measure similarity between a profile vector and an item vector, such as cosine similarity.

* Hybrid filtering combines content and collaborative filtering recommendations. Which filter to use depends on the real-world context — hybrid filtering may not always be the definitive answer.
Memory: You have 5GB RAM & need to train your model on a 10 GB dataset. How do you do this?

* SVM: a partial fit would work. The dataset could be split into several smaller-size datasets. Because SVM is a low-computational cost algorithm, it may be the best case in this scenario.

* If the data is not suitable for SVM, a Neural Network with a small batch size could be trained on a compressed NumPy array. NumPy has several tools for compressing large datasets, which are integrated into common neural network packages like Keras/TensorFlow and PyTorch.
What is the consequence of not setting an accurate learning rate?

If the learning rate it too low, the training of the model will progress very slowly, as the weights are making minimal updates. However, if the learning rate is set too high, this may cause the loss function to jump erratically due to drastic updates in weights. The model may also fail to converge to an error or may even diverge in the case that the data is too chaotic for the network to train.
Validation: Compare test sets & validation sets

* A test set is used to evaluate a model’s performance after training.
* A validation set is used during training for parameter selection and to prevent overfitting on the training set.

-->
Vagrant tutorial
categories:
tags: devops  tools  vagrant
date: 14 Apr 2020
slug:vagrant-tutorial
`\$vagrant [cmnd[opts]]`
`box` `cloud` `connect` `destroy` `global-status` `halt` `init` `login` `package` `plugin` `port` `powershell` `provision` `rdp` `reload` `resume` `share` `snapshot` `ssh` `ssh-config` `status` `suspend` `up` `upload` `validate` `version` (more)
• over HTTP
• over SSH
• Connect
• Security
• Custom Providers
• Configuration
• Minimum Vagrant Version
• Tips & Tricks
• `config.vm`
• `config.ssh`
• `config.wimrm`
• `config.winssh`
• `config.vagrant`
• Versioning
• Creating
• File Format
• Info Format
• Basics
• Files
• Shells
• intro to Ansible
• Ansible local
• Common Ansible options
• CFEngine
• Chef - common configuration
• Chef Solo
• Chef Zero
• Chef Client
• Docker
• Podman
• Puppet Apply
• Puppet Agent
• Salt
• Basics
• Port forwarding
• Private Networks
• Public Networks
• Basics
• NFS
• RSync
• SMB
• VirtualBox
• Overview
• Configuration
• Usage
• Overview
• Configuration
• Usage
• VirtualBox
• Hyper-V
• VMware
• Defining
• Controls
• Machine Communications
• Primary Machines
• Autostarting
• Install
• Basic Usage
• Configuration
• Default
• VirtualBox
• VMware
• Docker
• Hyper-V
• Custom
• Usage
• Design Basics
• Action Hooks
• Commands
• Configuration
• Guests
• Guest Capabilities
• Hosts
• Host Capabilities
• Providers
• Provisioners
• Packaging & Distribution
• FTP, SFTP
• Heroku
• Local execution
• Configuration
• Usage
• `cloud_init`
• `dependency_provisioners`
• `disks`
• `typed_triggers`
• Installation
• Windows access
• `PATH` mods
• Synced folders
• Using Docker

• -->
Insurance Pricing with Tweedie
categories:
tags: machine-learning  r  risk
date: 21 Apr 2020
slug:insurance-pricing-tweedie
Poisson; p=2 --> gamma, p=3 --> invGaussian

-->
Customer Review Responses
categories:
tags: custsvc  prodmgmt
date: 14 May 2020
slug:customer-reviews

Checklist Manifesto book summary (pdf)
categories:
tags: best-practices  execution
date: 14 May 2020
slug:checklist-manifesto

Kawaii product design
categories:
tags: design  uiux
date: 20 May 2020
slug:kawaii-design-principles

How did King Arthur flour do it?
categories:
tags: prodmgmt
date: 24 May 2020
slug:king-arthur-flour

Salience - The psychology of an experience you can’t ignore
categories:
tags: behavior  uiux
date: 27 May 2020
slug:ux-salience

Auctions and Private Sales
categories:
tags: auctions  economics  game-theory
date: 28 May 2020
slug:auctions-private

How Tuesday Morning went bankrupt
categories:
tags: finance  prodmgmt  retail
date: 28 May 2020
slug:tuesday-morning

Vickery Auctions and Custom Keyboards
categories:
tags: auctions  game-theory
date: 03 Jun 2020
slug:auctions-vickery-keyboards

Dollar Store Economics
categories:
tags: economics  prodmgmt  retail
date: 04 Jun 2020
slug:dollar-stores

How to Change Somebody's Mind
categories:
tags: behavior  influence  persuasion
date: 06 Jun 2020
slug:chg-somebodys-mind

Chrome extensions performance report
categories:
tags: webdev
date: 15 Jun 2020
slug:chrome-extension-metrics

Bundling primer
categories:
tags: prodmgmt
date: 18 Jun 2020
slug:bundling

What are Loaded Questions?
categories:
tags: behavior  interrogation
date: 18 Jun 2020

A History of Door Handles
categories:
tags: design  uiux
date: 18 Jun 2020
slug:door-handles

Social Media in China Survey - 2020
categories:
tags: china  social-media
date: 18 Jun 2020
slug:kawo-social-media-china

Why Figma Wins
categories:
tags: design  platforms  prodmgmt
date: 20 Jun 2020
slug:figma

Domain-Specific Processor Architectures (ACM)
categories:
tags: cpus  semiconductors
date: 21 Jun 2020
slug:chip-architectures

TSMC 7FF std cell library density (Semiwiki)
categories:
tags: semiconductors
date: 22 Jun 2020
slug:tsmc-7ff-stdcell-density

WireViz (GitHub)
categories:
tags: electronics  tools
date: 23 Jun 2020
slug:wireviz

5000 Years of Debt
categories:
tags: finance
date: 27 Jun 2020
slug:debt-5000-years

Negotiating like a Master - Stalin at Yalta
categories:
tags: negotiation
date: 02 Jul 2020
slug:stalin-at-yalta

Don't Force Users to Read PDFs Online (NN Group)
categories:
tags: pdf  uiux
date: 04 Jul 2020
slug:uiux-pdfs

Great Products don't Need to be Good Products (2010)
categories:
tags: focus  prodmgmt
date: 04 Jul 2020
slug:good-products-vs-great-products

Amazon exec memos - and narrative (Anecdote.com)
categories:
tags: prodmgmt  storytelling
date: 05 Jul 2020
slug:amazon-storytelling-narrative

How Cars and Hygiene Killed the Middle-Class Hat
categories:
tags: behavior  history
date: 10 Jul 2020
slug:behavior-hats-hygenie

Why is the Toy Industry so Hard?
categories:
tags: behavior  prodmgmt
date: 11 Jul 2020
slug:toy-industry

Do not remain nameless to yourself
categories:
date: 12 Jul 2020
slug:ideas-feynman-nameless

The Polymath's Playbook
categories:
tags: creativity  ideas
date: 12 Jul 2020
slug:polymath-playbook

Better than Free (kk.org)
categories:
tags: platforms  prodmgmt
date: 12 Jul 2020
slug:prodmgmt-better-than-free

x87, the floppy disk of instruction sets (evan miller)
categories:
tags: cpus  semiconductors
date: 14 Jul 2020
slug:chips-x87-floppy-disk

How Nespresso's coffee revolution got ground down
categories:
tags: prodmgmt  uiux
date: 15 Jul 2020
slug:nespresso-prodmgmt

Linux Servers - SSH Hardening Tips
categories:
tags: devops  linux
date: 15 Jul 2020
slug:linux-ssh-hardening

OKRs are not for everyone
categories:
tags: execution  prodmgmt
date: 16 Jul 2020
slug:okrs-prodmgmt

The Adjacent User Theory
categories:
tags: personas  prodmgmt
date: 16 Jul 2020

Visualization Catalog
categories:
tags: uiux  visualization
date: 17 Jul 2020
slug:viz-catalog

RBS - Ruby v3's type signature language
categories:
tags: ruby
date: 28 Jul 2020
slug:ruby3-types

10 modern layouts in 1 line of CSS
categories:
tags: css  html
date: 30 Jul 2020
slug:css-layouts

The UX of LEGO Interface Panels
categories:
tags: uiux
date: 01 Aug 2020
slug:uiux-legos-panels

15 Command Line improvments
categories:
tags: linux
date: 02 Aug 2020
slug:cmndline-tools

Pawnshop Economics
categories:
tags: pricing  prodmgmt
date: 05 Aug 2020
slug:pawnshop-pricing

Ecommerce Intellectual Property Primer
categories:
tags: ecommerce  prodmgmt
date: 10 Aug 2020
slug:prodmgmt-ecommerce-ip

Best Landing Page builders, 2020 edition
categories:
tags: html  prodmgmt  webdev
date: 17 Aug 2020
slug:prodmgmt-landing-pages

TikTok - Seeing Like an Algorithm
categories:
tags: tiktok  uiux
date: 22 Sep 2020
slug:tiktok-ux

Charisma - Essential Reads
categories:
tags: behavior  charisma
date: 23 Sep 2020
slug:charisma

Vagrant CLI cheatsheet
categories:
tags: devops  vagrant
date: 24 Sep 2020
slug:vagrant-cli-cheatsheet

From Fluffy to Valuable - How the Brain Recognizes Objects
categories:
tags: cognition
date: 11 Oct 2020
slug:cognition-object-recognition

Facial Recognition - Types of Attacks and Anti-Spoofing Techniques
categories:
tags: deep-learning  machine-vision
date: 11 Oct 2020
slug:deepfake-techniques

How to Win a Supreme Court Case
categories:
tags: behavior  persuasion
date: 19 Oct 2020
slug:persuasion-supreme-court

Lessons from Onboarding at Shopify
categories:
tags: prodmgmt  shopify
date: 27 Oct 2020
slug:onboarding-shopify

The freedom - and obligation - to dissent
categories:
date: 27 Oct 2020
slug:culture-dissent

A Summary of Poker Tells by Mike Caro
categories:
tags: behavior
date: 25 Nov 2020
slug:poker-tells

Up-sampling with Transposed Convolutions
categories:
tags: deep-learning
date: 01 Jan 2021
slug:upsampling

AWS, Parler and ToS
categories:
tags: aws
date: 10 Jan 2021
slug:corey-aws-parler

Glossary of adversarial nets / GANs articles
categories:
tags: deep-learning  gans
date: 03 Feb 2021

Activation function articles
categories:
tags: deep-learning  machine-learning
date: 03 Feb 2021
slug:activation-functions

Writing articles
categories:
tags: writing
date: 11 Feb 2021
slug:writing

Yes, you can Bullshit a Bullshitter
categories:
tags: behavior
date: 07 Mar 2021
slug:bullshitting

People really don't know when to shut up
categories:
tags: behavior  speaking
date: 07 Mar 2021
slug:stop-talking

You Don't Need a New Category
categories:
tags: prodmgmt
date: 08 Mar 2021
slug:category-kogan-prodmgmt

8 Powerful Storytelling Hooks
categories:
tags: storytelling
date: 13 Mar 2021
slug:storytelling

ML Cheatsheet (pdf)
categories:
tags: deep-learning  machine-learning
date: 28 Mar 2021
slug:ml-cheatsheet

categories:
date: 06 Apr 2021

categories:
tags: algorithms  machine-learning
date: 30 Apr 2021

categories:
tags: data-structures  machine-learning
date: 30 Apr 2021

Language, Linguisitcs & Symbols (May2021)
categories:
tags: language  linguistics  symbols
date: 03 May 2021
slug:language

Risk Management & Usage Pricing
categories:
tags: pricing  prodmgmt  risk
date: 04 May 2021
slug:usage-pricing-riskmgmt

Rails 6 with Webpacker startup issues
categories:
tags: rubyonrails
date: 08 May 2021
slug:rails6-webpacker

Visual Vocabulary
categories:
tags: visualization
date: 13 May 2021
slug:visual-vocabulary

How to replace text in multiple files using SED
categories:
tags: linux  sed
date: 15 May 2021
slug:sed-tip

categories:
tags: machine-learning  pycaret  python
date: 02 Jun 2021
slug:pycaret

Chivalry (aka Character)
categories:
tags: behavior
date: 16 Jun 2021
slug:chivalry

Chip Design Articles
categories:
tags: semiconductors
date: 16 Jun 2021
slug:chip-design

Charisma
categories:
tags: behavior  charisma  influence  persuasion
date: 16 Jun 2021
slug:charisma

Beliefs
categories:
tags: behavior
date: 16 Jun 2021
slug:beliefs

Antenna articles (pocket repo, 2021)
categories:
tags: antennas  electronics
date: 16 Jun 2021
slug:antennas

Animation
categories:
tags: animation
date: 16 Jun 2021
slug:animation

A-B Testing
categories:
tags: analytics
date: 16 Jun 2021
slug:ab-testing

Virality and Network Effects
categories:
tags: prodmgmt  virality
date: 21 Jun 2021
slug:virality

Bragging
categories:
tags: behavior  bragging
date: 21 Jun 2021
slug:bragging

Stoicism
categories:
tags: behavior  stoicism
date: 21 Jun 2021
slug:stoicism

Language, Linguistics & Symbols (Jun2021)
categories:
tags: language  linguistics  symbols
date: 21 Jun 2021
slug:language-linguistics

Shipping Cost Ideas (pdf)
categories:
tags: prodmgmt  supply-chain
date: 26 Jun 2021
slug:prodmgmt-shipcosts

Product Embeddings for E-Commerce (ArXiV)
categories:
tags: data-science  ecommerce  prodmgmt
date: 26 Jun 2021
slug:arxiv-prod-embeddings

Information Theory Tutorial (pdf)
categories:
tags: algorithms
date: 28 Jun 2021
slug:info-theory-tutorial

Streamlit
categories:
tags: python  streamlit  webdev
date: 30 Jun 2021
slug:streamlit

Tiktok's social graph sidestep
categories:
tags: platforms  prodmgmt  tiktok
date: 18 Aug 2021
slug:tiktok-social-graph

My Github Repos
categories:
tags: elixir  gatsbyjs  javascript  jekyll  jupyter  matplotlib  nextjs  pycaret  python  ruby  rubyonrails  scikit-learn  spacy
date: 01 Oct 2021
slug:github-repos

Data Science Interview Q&A
categories:
tags: data-science  machine-learning
date: 01 Oct 2021
slug:Data-Science-Interview-Questions

DL with Python & DL with PyTorch - book notes
categories:
tags: booknotes  deep-learning
date: 21 Oct 2021
slug:DL-python-pytorch-booknotes

ML project from scratch
categories:
tags: machine-learning
date: 21 Oct 2021
slug:ml-project-from-scratch

Seal Fit training skills (Casey Graham)
categories:
tags: motivation
date: 14 Nov 2021
slug:casey-graham-seal-training-skills

UI/UX Resources - Jul2022
categories:
tags: uiux
date: 02 Jul 2022
slug:uiux-oldpage

My Account dropdown
Order returns
Order tracking
Orders overview
Stored credit cards
ambiguity
analogies
animation
attention
autocompletion
barcodes
behaviors
benchmarking
best practices & checklists
biases
biases (social)
buttons
cards
change blindness
choices
cognition/perception
colors
community patterns
creepiness
critical incident technique
cultures
dark patterns
design guides
design patterns
design patterns (web)
ecommerce
ecommerce page examples (Baymard)
elements
elements/forms
empathy
explainers
eye movement
fidelity
flat design
frameworks
frameworks, tools
friction
front end design
game patterns
grids
html
images & photos
info design
interactions
intuition
job / career
jobs to be done
kerning
knolling
landing pages
language
learning
locality
logos
lorem ipsum
mass-market products
microinteractions
minimalism
mobile e-commerce
onboarding
performance
personas
platforms
principles, guidelines, frameworks
privacy
product lists & filtering
product pages
progressive disclosure
reputational UI elements
resources
responsive UI
rewards
scenario maps
shopping carts
similarity
sketching
social interaction elements
spatial memory
stories
style guides
tables
tbd
tools
touch
typography
usability
use cases
visual Hierarchy
visualization
whitespace
wireframes
word clouds

-->
Language & linguistics resources
categories:
tags: language  linguistics
date: 02 Jul 2022
slug:language-oldpage

Creativity & Innovation
categories:
tags: creativity  ideas  innovation
date: 02 Jul 2022
slug:creativity-oldpage

Do you know how to "read" a face?
categories:
tags: behavior  emotion  interrogation  interviewing
date: 04 Jul 2022
slug:behavior-spaff

Ideas and Learning links - 2019
categories:
tags: ideas  learning
date: 28 Jul 2022
slug:ideas

Rails & Rubygems
categories:
tags: rubygems  rubyonrails
date: 17 Aug 2022
slug:rails-rubygems

## Rails & RubyGems resources

active record
active storage
api clients
api-only apps
assets (JS, images, CSS)
associations
background jobs
caching
callbacks
code design
concurrency, parallelism
configuration
configuration - routing
controllers
css
data models
datasets
db options
db queries
db schema migrations
db seeds
debugging
deployment - devops
documentation
ecommerce, payments
email
generators & templates
graphics, pdfs, images
internationalization
javascript
logging
markup
modules
monitoring
ocr
optimization
pdfs
publishing
rack middleware
rails CLI
rails v6
rails v7
rails websockets (active cable)
revision control
ruby language extensions
rubygems
scaffolds
security
testing
tutorials & resources
validations
views (HTML forms)
views (HTML helpers)
views (layouts & rendering)
visualization
web servers

-->
20 useful Python libraries
categories:
tags: python
date: 15 Sep 2022
slug:python-libs

Linear Algebra, Machine Learning, Deep Learning articles (originally posted Dec2019)
categories:
tags: algorithms  deep-learning  linear-algebra  machine-learning  pandas
date: 17 Jan 2023
slug:math-bestof

Book chapter summaries - deep learning, machine learning, various math
categories:
tags: algorithms  bandits  deep-learning  linear-algebra  machine-learning  probability
date: 18 Jan 2023
slug:math-booknotes

Source abbreviations:    AJE: Algorithms     BA: Bandit Algorithms    BJP: (me)    CI: Collective Intelligence     CO: Convex Estimation    DIDL: Dive into Deep Learning    DLG: Deep Learning (Goodfellow, et al)    DMMD: Data Mining of Massive Datasets    DSA: Data structures & Algorithms    DSCL: Data Science at the Command Line    EA: Elementary Algorithms    ESL: Elements of Statistical Learning    FDS: Foundations of Data Science    GT: Geometric Topology    ITA: Intro to Algorithms     JE: Algorithms     NP: Numeric Python     SKL: Scikit-learn     SM: ML cheatsheet     RL: Reinforcement Learning

# Book chapter summaries - deep learning, machine learning, various math

(multiple)
data science cheatsheet 2.0 (aaron wang)
distributions; hypothesis testing; concepts; model evaluation; linear regression; logistic regression; decision trees; naive bayes; svms; knns; clustering; dimensional reduction (PCA, LDA, FA); NLP; neural nets (basics, CNNs, RNNs); boosting; recommenders; reinforcement learning; anomoly detection

other topics (FDS)
ranking & social choice; compressed sensing & sparse vectors; use cases; an uncertainty principle; gradients; linear programming; integer optimization; semi-definite programming

approximations
approximate-inference (DLG)
inference as optimization
expectation maximization (EM)
MAP inference | sparse coding
variational inference
learned approx inference

approximations (algorithm reductions) (ADM)
algo reductions
basic hardness reductions
satisfiability
creative reductions
"proving" hardness
P vs NP hardness
NP-complete problems

approximations (algorithm reductions) (ITA)
the vertex-cover problem
the traveling salesman problem
the set-cover problem
randomization & linear programming
the subset-sum problem

arithmetic
complex-numbers (LAY)
examples; geometric representation; powers; R^2

computation (DLG)
underflow, overflow
poor conditioning
jacobian & hessian matrices
constrained optimization
linear least squares

is n a prime number? if not, what are its factors?

linear algebra (LAY)
linear equations
row reductions
vector equations
Ax=b
solution sets of linear systems
applications
linear independence
linear transforms
linear models - business, science, engineering

linear equation solvers (ADM)
if A = an mxm matrix, and b = an mx1 vector, what is vector X such that AX=b?

number theory (ITA)
basics (divisors, primes/composites)
greatest common divisor (Euclid)
modular math (group theory?)
linear equations
the chinese remainder problem
powers
RSA public-key crypto
prime testing
factorization (integer)

(also part of "numericals" chapter of ADM.)

association rules
association rules | market basket analysis (ESL)
frequent itemsets (DMMD)
market-basket modeling; association rules; a-priori algorithm; large datasets & main memory; limited-pass algorithms; counting items in streams

autoencoders
autoencoders (DLG)
undercomplete AEs; regularized AEs; representational power, layer size & depth; stochastic encoders & decoders; denoising AEs; learning manifolds with AEs; predictive sparse decomposition; applications

autoencoders with Tensorflow (HoML)
bandit algorithms
bash
common linux/bash commands (Data Science - Command Line)
environment (alias, bash, cols, for, sudo, ...)
files & directories (body, cd, cat, chmod, ...)
pattern matching (awk, sed, grep)
deployment (aws, git, )
CSV data
JSON data
online data (curl, scp, scrape, ssh)
integer/date sequences,br> file extraction/compression (tar, tree, uniq, ...)

bayes
bayes inference (CSI)
two examples
uninformed prior distributions
flaws in frequentist inference
bayes vs frequentist comparison

bayes nets (directed graphs) (SM)
bayes statistics (NP)
intro & model definition
sampling posterior distributions
linear regression

bayesian statistics (SM)
intro
posterior distribution
MAP estimates
bayes model selection
priors
hierarchical bayes
empirical bayes
decision theory

cheatsheets
deep learning cheatsheet (2018) (SCDL)
CNNs, RNNs, tips & tricks

sampling methods (PSC)
inverse transform sampling; the bootstrap; rejection sampling; importance sampling

classification
cal housing market analysis (HoML)
classification basics (HoML)
MNIST, aka hello world
confusion matrix
metrics (precision,recall)
ROC curve
multiclass classification
multilabel classification
multioutput classification

discriminants (LDA, QDA) (SKL)
Linear DA

linear classification (ESL)
regression - indicator matrix
linear discriminant analysis (LDA)
logistic regression
hyperplanes

logistic regression (SKL)
solvers - liblinear, newton-cg, lbfgs, sag, saga

metrics (SKL)
accuracy, top-K accuracy, balanced accuracy
cohen's kappa, confusion matrix, classification report
hamming loss, precision, recall, f-measure
precision-recall curve, avg precision
precision-recall curve (multilabel)
jaccard similarity
hinge loss
log loss
matthews correleation coefficient
confusion matrix (multilabel)
ROC curve
zero-one loss
brier score

multiclass & multioutput algos (SKL)
intro
multiclass (aka label binarization)
one-vs-rest
multilabel
one-vs-one
output code
multioutput
classifier chains

multilayer perceptron (MLP) (SKL)
naive bayes (SKL)
NB classification (gaussian, multinomial, complement, bernoulli)
categorical NB

nearest neighbors (SKL)
basic algos (ball tree, KD tree, ...)
KNNs & radius-based algos
nearest centroids
neighborhood components analysis (NCA)

nearest neighbors (ESL)
prototype methods (kmeans, learning vector quant, gaussian mixtures)
knn classifiers
computational performance

clustering
biclustering methods (SKL)
intro, spectral co/biclustering

clustering (DMMD)
intro (data, strategies, dimensionality)
hierarchical
k-means
CURE (clustering using representatives)
non-euclidean spaces
clustering for streams & parallelism

clustering (FDS)
intro
k-means (lloyds algo, wards algo)
k-center
low-error
spectral
approximation stability
high-density
kernel methods
recursive clustering w/ sparse cuts
dense submatrices & communities
community finding & graph partitions
spectral clustering & social nets

clustering (ESL)
clustering methods (SKL)
Kmeans & Kmeans minibatch
Affinity propagation
Mean shifts
Spectral clustering
Agglomerative clustering
Hierarchical clustering
DBSCAN
Birch
OPTICS

clustering metrics (SKL)
rand index; mutual info score; homogeneity / completeness / v-measure; Fowlkes-Mallows score; silhouette coefficient; Calinski-Harabasz index; Davies-Bouldin index

combinationals
given a directed acyclic graph (vertices = jobs, edges = task dependencies), what schedule completes the job in minimum time/effort?

given integer n, generate partitions that add up to n.

given n, generate a set of items of length n.

given a set of logical constraints, is there a configuration that satisfies the set?

computation - complexity - performance - benchmarking
data structures
datasets
deep learning architectures
CNN cheatsheet (SCDL)
convolutional NNs (DLG)
convolutionl NNs (DLG)
deep feedforward NNs (DLG)
deep generative models (DLG)
deep learning (DLG)
gans (DIDL)
intro (ESL)
intro to neural nets (CSI)
intro; fitting; autoencoders; deep learning; learning (dropout, input distortion)

linear NNs (DIDL)
neural network zoo (asimov institute)
perceptrons (DIDL)
representation learning (DLG)
greedy layer-wise unsupervised pretraining
transfer learning | domain adaptation
semi-supervised disentangling of causal factors
distributed representation
exponential gains from depth
providing clues to find underlying causes

structured probabilistic models (DLG)
challenges; using graphs; sampling from graphs; advantages; dependencies; infererence & approx inference

density estimation
density estimates (PSC)
density estimates
histograms
kernel density estimator (KDE)

density estimation methods (SKL)
intro, histograms, kernel density estimates (KDE)

design
dimensional reduction
dynamic programming
dynamic programming (ITA)
dynamic programming (JE)
intro; faster fibonacci numbers; smart recursion; greed is stupid; longest increasing subsequence; edit distance; subset sum; binary search trees; dynamic programming on trees;

ensembles
evaluation
feature engineering
file I/O
data I/O (DSCL)
local data to docker
decompressions (zip, ...)
excel to CSV
relational DBs
web APIs
authentication
streaming APIs

file I/O (NP)
CSV; HDF5; h5py; Pytables; serialization

file I/O - datatypes (PDA)
text files; JSON; XML/HTML scraping; binary data; web APIs; databases

gaussians
generative models
generative models - discrete data (SM)
generative classifiers; bayesian concept learning; beta-binomial model; dirichlet-multinomial model; naive bayes classifiers

geometry
given n items and m bins - store all the items using the smallest number of bins.

geometry (ITA)
medial axis xforms (ADM)
spatial structures (DSA)
multi-dimensional structures; planar straight-line graphs; search trees; quad/octal trees; binary space partitioning trees; r-trees; spatio-temporal data; kinetic structures; online dicts; cuttings; approximate geometric queries

vector spaces (LAY)
graphs
basic algorithms (JE)
definitions; representations; data structures; whatever-first search; depth-first; breadth-first; best-first; disconnected graphs; directed graphs
reductions (flood fill)

given a graph, finding the shortest path touching each edge.

how to find the largest clique (cluster) in a graph?

find the pieces of a graph, where vertices x & y are members of different components if no path exists from x to y.

what's the smallest set of colors needed to color the edges of a graph, such that no two same-color edges share a common vertex?

edge vertex connectivity (ADM)
what's the smallest subset of vertices (edges) whose deletion will disconnect a graph?

feedback edge vertex set (ADM)
flows & cuts applications (JE)
edge-disjoint paths
vertex capacities & vertex-disjoint paths
bipartite matching
tuple selection
disjoint-path covers
baseball elimination
project selection

graph algos (ITA)
representations; breadth-first search; depth-first search; topological sorting; strongly-connected components;

graph algos (SOTA) (paperswithcode)

given two graphs G & H, find a function from G's vertices to H's vertices such that G & H are identical.

graph link analysis (DMMD)
PageRank; link spam; hubs & authorities

given a weighted graph G and integers k & m, partition the vertices of G into m equally-sized subsets such that the total edge cost spanning the subsets is at most k.

graphs connected components (ADM)
graphs polynomial time (ADM)
graphviz (tool) (graphviz)
maxflow (ITA)
min spanning trees (JE)
min spanning trees (ITA)
minimum spanning tree (ADM)
random graphs (FDS)
social graphs (DMMD)
sparse matrices graphs (NP)
undirected graphs (ESL)
greedy algos
inference
after-model-selection-estimation (CSI)
accuracy after model selection
selection bias
combined bayes-frequentist estimation
notes

inference & max likelihood (ESL)
inference frequentist (CSI)
parametric inference (PSC)
information theory
info theory tutorial (stone, USheffield)
finding a route
bits are not binary digits
entropy
entropy - continuous variables
max-entropy distributions
channel capacity
shannon's source coding theorem
noise reduces channel capacity
mutual info
shannon's noisy channel coding theorem
gaussian channels
fourier analysis
history
key equations

interviewing
kernels
label spreading, label propagation
latent variables
linear factor models (DLG)
probabilistic PCA + factor analysis
independent component analysis
sparse coding
manifold representation of PCA

learning
linear models
generalized linear models (SM)
(incomplete notes in orig PDF)

linear programming
make
intro to make (DSCL)
overview|intro; running tasks; building; dependencies; summary

markov chains
matrix math
basics (DIDL)
linear & matrix ops
eigen decompositions
single-variable calculus
multi-variable calculus
integrals
random variables

determinants (LAY)
eigenvectors & eigenvalues (LAY)
intro; eigenvectors & difference equations
determinants & characteristic equations
similarity
diagonalization
eigenvectors & linear transforms
complex eigenvalues
discrete dynamical systems
differential equations
iterative estimates

inner-product-length-orthogonality (LAW)
linear algebra overview (DLG)
scalars, vectors, matrices, tensors
vector|matrix multiplication
identity matrix
inverse matrix
linear dependence
span
norms
diagonal matrix
symmetric matrix
orthogonal matrix
eigen decomposition
singular value decomposition (svd)
moore-penrose pseudoinverse matrix
trace operator
determinant
example - principal components analysis (PCA)

matrix cookbook (matrixcookbook.com)
basics
derivatives
inverses
complex matrices
solutions & decompositions
multivariate distributions
gaussians
special matrices
functions & operators
1-D results
proofs

matrix math (LAY)
matrix ops (ITA)
linear equations
bandwidth reduction
matrix multiplication
determinants & permanents
optimization (constrained, unconstrained)
linear programming
random number gen
factors & prime testing
arbitrary-precision math
the knapsack problem
discrete fourier transforms (DFTs)

symmetric matrices (LAY)
max likelihood estimation (MLE)
methods
methodologies (paperswithcode)
representation learning; transfer learning; image classification; reinforcement learning; 2D classification; domain adaptation; data augmentation; ...

mixtures
latent linear models (SM)
factor analysis
principal components analysis (PCA)
choosing number of dimensions
PCA for categories
PCA for paired & multiview data
independent component analysis (ICA)

monte carlo
monte carlo methods (DLG)
sampling; importance sampling; markov chain monte carlo (MCMC); gibbs sampling; mixing challenges

multilabel
natural language processing
Gensim lessons ()
NLP SOTA (paperswithcode)

natural language processing (NLP) (DIDL)
spaCy tutorial (spacy.io)
topic models (FDS)
topic models
non-negative matrix factorization (NMF)
hard & soft clustering
latent dirichlet allocation (LDA)
math
term-topic matrices
hidden markov models
graph models & belief propagation
bayes|belief nets
markov random fields
factor graphs
tree algorithms
message passing
single-cycle graphs
max weight matching
warning propagation
variable correlation

novelties-outliers
numerical analysis
numpy
ndarray internals
array manipulation
ufuncs
structured & record arrays
sorting
numba
performance tips

basics (PDA)
numpy basics (PDSH)
arrays; boolean arrays; broadcasting; indexing; sorting; structured data; aggregations; ufuncs; data types

vectors, matrices, ndarrays (NP)
pandas
pandas basics (PDA)
series; data frames; index objects; essential functions; descriptive stats

pandas basics (PDSH)
aggregation/grouping, concat, append, hierarchical indexes, merge, join, missing values, objects, ops, performance, pivot tables, time series ops, vectorized string ops

parametric models
performance
planning
planning algorithms (LaValle)
intro
motion planning
decision theory
differential-constraint planning

planning / capacity
probabilistic analysis
Probabilistic Analysis and Randomized Algorithms (ITA)
Indicator random variables, Randomized algorithms, Probabilistic analysis and further uses of indicator random variables

probability & statistics
pycaret
PyCaret intro (BJP)
PyCaret is a high-level, low-code Python library that makes it easy to compare, train, evaluate, tune, and deploy machine learning models with only a few lines of code. At its core, PyCaret is basically just a large wrapper over many data science libraries such as Scikit-learn, Yellowbrick, SHAP, Optuna, and Spacy. Yes, you could use these libraries for the same tasks, but if you don’t want to write a lot of code, PyCaret could save you a lot of time.

recommenders
recurrent NNs
recursion
backtracking (AJE)
backtracking (JE)
recursion (JE)
reductions
simplify & delegate
tower of hanoi
mergesort
quicksort
design pattern
recursion trees
linear-time selection
fast multiplication
exponentiation

regression
reinforcement learning
restricted boltzmann machines
robotics
searching & sorting
set theory
streams
strings
survival analysis
svms
support vector machines (ESL)
support vector machines (SVMs) (SKL)
classification (SVC, NuSVC, LinearSVC)
multiclass SVM
scoring & metrics
weighted classes/samples
regression (SVR, NuSVR, LinearSVR)
complexity
kernels
precomputed kernels - the Gram matrix

svms (HoML)
sympy
intro (NP)
symbols; expressions; numeric evaluation; calculus (derivatives, integrals, series expansions, limits, sums & products); equation solvers; linear algebra

tbd
tensorflow
time series
time series (PSC)
time series applications (SOTA) (paperswithcode)
time series ops (PDA)
date & time datatypes; ranges, frequencies & shifting; periods; frequency conversion; moving windows

tools
topology
hyperbolic topology (GT)
groups; spaces; manifolds; thick-thin decomposition; sphere at infinity

surfaces (GT)
intro; teichmuller spaces; surface diffeomorphisms

three-manifolds (GT)
topology; seifert manifolds; construction; the "eight geometries"; mostow rigidity problem; hyperbolic 3Ms; hyperbolic dehn filling

training
use cases
vision
computer vision SOTA (paperswithcode)

developers tools (scikit-image)
edges & lines (scikit-image)
contour finding
convex hulls (binary images)
canny filters
marching cubes
ridge operators
active contour model
drawing std shapes
random shapes
hough transforms (straight line)
approximating & subdividing polygons
hough transforms (circular, elliptical)
skeletonizing
morphological thinning
edge operations (multiple)

exposures & colors (scikit-image)
RGB-grayscale conversions
RGB-HSV conversions
histogram matching
(ex) immunohistochemical (IHC) staining
adapting grayscale filters to RGB images
regional maxima filtering (bright features)
local histogram equalization (LHE)
gamma & log-contrast adjustments
histogram equalization
tinting grayscale images

filtering & restoration (scikit-image)
image datasets (scikit-image)
longform examples (scikit-image)
numpy basic ops (scikit-image)
object detection (scikit-image)
object segmentation (scikit-image)
transforms & registration (scikit-image)
visualization
wavelets

-->
Behavior & Emotion resources (updated)
categories:
tags: behavior
date: 21 Jan 2023
slug:behaviors-oldpage
Self-Appointed Geniuses (Priceonomics)
arguments, conflicts
attention
attitude
beliefs
bias
bragging
bystander effect
Why "Open Secrets" exist in Organizations
charisma
charity, chivalry, values
complements
How to Give Compliments (Less Penguiny)
concepts
conceptually.org
coolness, desire, envy
creativity
criticism, feedback
delegation
distractions
How the Brain Ignores Distractions (MIT)
emotional intelligence
Why Emotional Intelligence is Important - 7 Reasons (Pocket/Fast Company)
empathy
failure
familiarity
The Science of Familiarity
fascination
gestures
getting things done
grit, hustle
habits
humility
influence
likeability
Loaded Questions - What They Are (Effectiviology)
loyalty, trust, honesty
memory-recall
mental models
mentoring
motivations
persuasion
power, respect
premature optimization
Premature Optimization (Effectiviology)
pressure
prospect theory
What is Prospect Theory?
reactance
Reactance (Wikipedia)
rejection
rituals
scarcity
shame
signaling
social proof
social skills
Improve Your Social Skills - A Guide
speaking
spin, subterfuge
stoicism
surprise
symbols

-->
The Art of Memory - Mnemonic Techniques (updated)
categories:
tags: behavior  memory
date: 23 Jan 2023
slug:memory-mnemonics

Intro to Elixir - My GitHub repo
categories:
tags: elixir
date: 23 Jan 2023
slug:elixir-intro

Understanding backward passes
categories:
tags: algorithms  deep-learning  machine-learning
date: 24 Jan 2023

Real-Time Hand Tracking with MediaPipe (GoogleBlog)
categories:
tags: deep-learning  machine-vision
date: 25 Jan 2023
slug:hand-tracking-with-mediapipe

What is Targeted Dropout?
categories:
tags: deep-learning
date: 26 Jan 2023
slug:targeted-dropout

Essential reads (UI/UX) (3/6/2020)
categories:
tags: uiux
date: 26 Jan 2023

Feature Engineering Articles (2019)
categories:
tags: feature-engineering  machine-learning
date: 26 Jan 2023
slug:feature-engineering

Golang resources
categories:
tags: golang
date: 26 Jan 2023
slug:golang-resources

Python (Numpy) resources
categories:
tags: numpy  python
date: 29 Jan 2023
slug:python-numpy
20 essential functions   (towards data science)
randint()
random()
random.randn()
ones()
identity()
arange()
full()
ravel()
reshape()
transpose()
vsplit()
hsplit()
concatenate()
vstack()
hstack()
det()
inv()
eig()
dot()
matmul()
(towards data science)
isclose()
intersect1d()
stack()
indexing   (towards data science)
intermediate   (python DS handbook)
arrays
boolean arrays
fancy indexes
sorting
structured data
aggregations
ufuncs
datatypes
matrix multiplication basics   (towards data science)
datatypes
typecasting
promoting
complex numbers
memory
arrays
indexes
slices
views
fancy indexing
boolean indexing
reshaping
merging
vectorization
math ops
aggregate ops
boolean arrays
conditionals
logic ops
set ops
matrix ops
tutorial   (python data science handbook)

-->
Supply Chain Resources
categories:
tags: supply-chain
date: 31 Jan 2023
slug:supplychain
Tags:
china   social   tools
china
social
tools

-->
Psychology for UX study guide (NN/g)
categories:
tags: behavior  emotion  uiux
date: 31 Jan 2023
slug:uiux-psych-studyguide

Feature engineering with Python
categories:
tags: feature-engineering  numpy  pandas  python  scikit-learn
date: 04 Feb 2023
slug:python-feature-engineering
category coding   (FE cookbook, 2nd ed (Packt))
setup, tips, caching, regression target transforms
creation   (FE cookbook, 2nd ed (Packt))
data imputation   (FE cookbook, 2nd ed (Packt))
data imputation basics   (scikit-learn 0.24)
univariate, multivariate, nearest-neighbor, marking imputed values
data transforms   (FE cookbook, 2nd ed (Packt))
datasets - simple examples   (scikit-learn 0.24)
iris, digits, cal housing, labeled faces, 20 newsgroups, (more)
date/time handling   (FE cookbook, 2nd ed (Packt))
discretization (binning)   (FE cookbook, 2nd ed (Packt))
feature engineering intro   (python DS handbook)
one-hot encoding, word counts, tf-idf, linear-to-polynomial, missing data, pipelines
feature extraction (text)   (scikit-learn 0.24)
bag of words, sparsity, vectorizers, stop words, tf-idf, decoding, applications, limits, the hashing trick, out-of-core ops
file i/o   (numeric python)
CSV, HDF5, h5py, pytables, hdfstore, JSON, serialization, pickle issues
outlier management   (FE cookbook, 2nd ed (Packt))
preprocessing basics   (scikit-learn 0.24)
mean removal, variance scaling, sparse scaling, outlier scaling, distribution maps, normalization, category coding, binning, binarization, polynomial features.
random projections   (scikit-learn 0.24)
scaling   (FE cookbook, 2nd ed (Packt))
tools (featuretools)   (FE cookbook, 2nd ed (Packt))
tools (tsfresh)   (FE cookbook, 2nd ed (Packt))

-->
Seaborn gallery
categories:
tags: machine-learning  python  seaborn  visualization
date: 04 Feb 2023
slug:seaborn-gallery

-->
Shame - as a tool
categories:
tags: behavior  shame
date: 04 Feb 2023
slug:behaviors-shaming

## Shame - as a tool

originally from Scientific American

-->
33 Strategies of War - booknotes
categories:
tags: behavior  booknotes  influence
date: 04 Feb 2023
slug:laws33war-booknotes

categories:
tags: behavior  movies  television
date: 04 Feb 2023
slug:acting

categories:
tags: jekyll  ruby
date: 04 Feb 2023
slug:sinatra

## Sinatra (Ruby site generator) resources

(sinatrarb)
(sinatrarb)
(github)
(shiroyasha)

-->
categories:
tags: jekyll  ruby
date: 04 Feb 2023
slug:jekyll

## Jekyll (Ruby static website generator) resources

categories
cheat sheet
collections
css
data
deployment
images
liquid
logic
plugins
posts
seo
tags
variables
yaml

-->
Optimizing for the speed of light
categories:
tags: devops
date: 04 Feb 2023
slug:optimize-speed-light

Activations (Machine Learning)
categories:
tags: machine-learning
date: 05 Feb 2023
slug:activations

What is an Empathy Map? (NN Group)
categories:
tags: empathy  uiux
date: 12 Feb 2023
slug:empathy-maps

Apparel Brand Scaling (pdf)
categories:
tags: ecommerce  fashion  prodmgmt
date: 20 Feb 2023
slug:apparel-brand-scaling

2020 Ecommerce Stats (pdf)
categories:
tags: ecommerce  prodmgmt
date: 20 Feb 2023
slug:prodmgmt-ecomm-stats

Web Design - Chrome Extensions
categories:
tags: tools  webdev
date: 20 Feb 2023
slug:chrome-extensions-design

Go By Example (Golang tutorial)
categories:
tags: golang
date: 20 Feb 2023
slug:golang-oldpage

Productivity articles
categories:
tags: behavior  productivity
date: 20 Feb 2023
slug:productivity
5 Whys (Karine Bengualid)
more..

When your productivity takes a nosedive, it adds stress and anxiety, as you don't have enough time to accomplish your goals and do what really matters to you. Understanding why your productivity is flailing will help you get back on track.

Being "Glue" (Denise Yu)
more..

IYour job title says "software engineer", but you seem to spend most of your time in meetings. You'd like to have time to code, but nobody else is onboarding the junior engineers, updating the roadmap, talking to the users, noticing the things that got dropped, asking questions on design documents, and making sure that everyone's going roughly in the same direction. If you stop doing those things, the team won't be as successful. But now someone's suggesting that you might be happier in a less technical role. If this describes you, congratulations: you're the glue. If it's not, have you thought about who is filling this role on your team?

GTD in 15 minutes – A Pragmatic Guide to Getting Things Done (Hamberg.no)
more..

GTD—or “Getting things done”—is a framework for organizing and tracking your tasks and projects. Its aim is a bit higher than just “getting things done”, though.

Scott Hanselman's List of Productivity Tips
more..

What follows is Danny Schreiber's summary of my Productivity Talk. If you'd like me to give a version of this talk at your company or event, contact me.

Henry Rollins on defining success
more..

Henry Rollins is an American musician, writer, actor, radio host, activist, spoken word artist, and comedian. He was the singer of the hardcore punk band Black Flag and later the Rollins Band among other solo projects and collaborations. He won a Grammy in 1995 for the spoken adaptation of his 1994 tour memoir, Get in the Van. Since the early 1980s he’s released too many things to list here.

Deep Work
more..

How to master the #1 job skill that will never be obsolete

Something Small. Every Day. (Austin Kleon)
more..

It takes time to do anything worthwhile, but thankfully, we don’t need it all in one chunk. So this year, forget about the year as a whole. Forget about months and forget about weeks. Focus on days.

The Dreaded Weekly Status Email (Elegant Hack)
more..

I remember the first time I had to write one of these puppies.

My Magic Response to "Can I Pick Your Brain?" (Stacking the Bricks)
more..

The first few times it happens, it feels like a positive signal.Somebody wants your advice and perspective. You must be good at what you do. And that’s gotta translate to your career…somehow, right?

Getting Ahead by Being Inefficient (Shane Parrish)
more..

Inefficient does not mean ineffective, and it is certainly not the same as lazy. You get things done – just not in the most effective way possible. You’re a bit sloppy, and use more energy. But don’t feel bad about it. There is real value in not being the best.

Hustle as Strategy (Tom Tunguz)
more..

In a world where there are no secrets, where innovations are quickly imitated or become obsolete, the theory of competitive advantage may have had its day. Realistically, ask yourself, If all your competitors gave their strategic plans to each other, would it really make a difference?

I'm not really a Good Web Developer, I'm Just Good at Googling things (dev-diaries)
more..

Being a web developer means having a good grasp on a wide array of topics: navigating the terminal, html, css, javascript, cloud infrastructure, deployment strategies, databases, HTTP protocols and that’s just the beginning.

Two Things to do After Every Meeting (Paul Axtell)
more..

Steve Jobs insisted that every item on a meeting agenda have a designated person responsible for that task and any follow-up work that happened. He called that person the DRI—the Directly Responsible Individual. He knew the public accountability would ensure that a project or task would actually get done, and he wanted to set clear, organized instructions for his team to follow.

2020's Best Productivity Apps (Kelsey Wroten)
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Let’s get one thing out of the way first: You do not need any of the apps on this list in order to be productive.

How to conquer work paralysis like Ernest Hemingway (Zaria Gorvett)
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The author wasn’t all about literary masterpieces, dry martinis and rakish charm – he also invented a technique that can beat procrastination and boost productivity.

Work is a Queue of Queues (Andrew Montalenti)
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Do you ever get that feeling like no matter how hard you work, you just can’t keep up? This isn’t a problem uniquely faced by modern knowledge workers. It’s also a characteristic of certain software systems. This state — of being perpetually behind on intended work-in-progress — can fall naturally out of the data structures used to design a software system. Perhaps by learning something about these data structures, we can learn something about the nature of work itself.

Productivity Lessons from Artists & Entrepreneurs (Brad Stulberg)
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Put simply, the overlap between professional, creative, and athletic success is huge. Here are a few timeless productivity lessons, or principles of performance, that apply no matter what you’re doing.

One man's obsessive 40-year pursuit of the productive life (Stephen Wolfram)
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Stephen Wolfram has always liked using technology to get stuff done and monitor personal progress. Here are the secrets that help him power through his workdays.

What Silicon Valley "Gets" about Software Engineers that Traditional Companies Do Not (Gergely Orosz)
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I've noticed that Silicon Valley companies consistently "get" a few things that their traditional counterparts fail to either understand or implement in practice - especially in Europe.

8760 Hours: How to get the most out of next year (Alex Vermeer)
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The end of a year is the perfect time to review one’s life, goals, plans, and projects, as well as plan for the upcoming year. I’ve been fine-tuning my own review process for several years and thought others might be interested to know what I do and how.

the diminishing returns of productivity culture (Anne Petersen)
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This is the midweek edition of Culture Study — the newsletter from Anne Helen Petersen, which you can read about here. If you like it and want more like it in your inbox, consider subscribing.

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categories:
tags: ecommerce  prodmgmt
date: 20 Feb 2023

Word Cloud Tools
categories:
tags: tools  uiux  webdev
date: 20 Feb 2023
slug:wordclouds

Python 3.8 Std Library
categories:
tags: python
date: 20 Feb 2023
slug:python-std38

The Remaking of Comedy Central (Vulture article)
categories:
tags: prodmgmt  storytelling  television
date: 21 Feb 2023
slug:comedy-central

Pricing algorithms & collusion
categories:
tags: algorithms  game-theory  pricing  prodmgmt
date: 21 Feb 2023
slug:pricing-collusion

Product Idea Generators
categories:
tags: ideas  innovation  prodmgmt
date: 21 Feb 2023
slug:prodmgmt-idea-generator

language taxonomy build tools
categories:
tags: language  nlp  tools
date: 05 Apr 2023
slug:taxonomy-build-tools

If Smiling is so easy to fake - Why do we fall for it?
categories:
tags: behavior  deceit
date: 08 Apr 2023
slug:smiles-deceit

Hypothesis testing - cheat sheet
categories:
tags: probability  statistics
date: 09 Apr 2023
slug:hypothesis-testing

ChatGPT items
categories:
tags: chatGPT  nlp  transformers
date: 16 Apr 2023
slug:chatgpt

Stuff to Read, 12/14/21 (revisited)
categories:
tags: algorithms  animals  cynicism  deep-learning  learning  log4j  neurology  public-policy  repair  webdev
date: 17 Apr 2023
uBlacklist (GitHub)

Blocks specific sites from appearing in Google search results

Leaving Quora After 10 Years of Answering Questions (Phil Jones)

If you are reading this, you probably know me from Quora where I spent over 10 years writing more than 11,000 answers. I'm writing this page because I will soon be gone from Quora.

What a Progressive Utopia Does to Outdoor Dining (Atlantic)

In San Francisco and elsewhere in California, the red tape that prevented dining alfresco before the pandemic is starting to grow back.

Opt Out of Cynicism (D13V)

Growing up in post-socialist-turned-cowboy-capitalist Bulgaria I grew up around a lot of cynical behavior and absorbed it deep into me. It was the water I was swimming in, and I knew no better. There was always this feeling that attempts at improvements are futile. If anyone tried to improve the system in any way, they will face a great opposition, and any value they bring forward will be immediately vultured away. This made it obvious for me to see how any changes will be abused and rendred futile. I also became good at rationalizing the existing status quo. There’s this example which stuck with me, that if someone created a coin operated parking meter, another one will quickly figure out how to steal the coins out of it. Thus, the attempt to bring order will fail, and the rationalization is that we are a motivated but backstabbing people which get in our own interest.

Aint No Party Like a 3rd Party (Adactio)

I’d like to tell you something not to do to make your website better. Don’t add any third-party scripts to your site.

Why Tacit Knowledge is More Important than Deliberate Practice (Commonplace)

I want to spend an essay talking about tacit knowledge, and why I think it is the most interesting topic in the domain of skill acquisition. If you are a longtime Commonplace reader, you’ll likely have come across this idea before, because I’ve written about it numerous times in the past. But I think it’s still good idea to dedicate a whole piece to the topic.

Anatomy of a GOAT: What Makes Magnus Carlsen the world's best Chess player (ESPN)

On Friday, needing just one point against Ian Nepomniachtchi to defend his world champion status, Magnus Carlsen closed the match out with three games to spare, 7.5-3.5. He's been the No 1 chess player in the world for a decade now and is in his eighth year as undisputed world champion.

Learn X in Y Minutes

Take a whirlwind tour of your next favorite language. Community-driven!

Dive Into Deep Learning (ebook) (d2l.ai)

Interactive deep learning book with code, math, and discussions. Implemented with NumPy/MXNet, PyTorch, and TensorFlow. Adopted at 300 universities from 55 countries.

I Have a Brain Injury (YouTube)

I got hit in the head by a falling pipe while shooting a video in July, and haven't been the same since...

How to Train your Decision-Making AIs (Gradient)

The combination of deep learning and decision learning has led to several impressive stories in decision-making AI research, including AIs that can play a variety of games (Atari video games, board games, complex real-time strategy game Starcraft II), control robots (in simulation and in the real world), and even fly a weather balloon. These are examples of sequential decision tasks, in which the AI agent needs to make a sequence of decisions to achieve its goal.

Advanced NLP with SpaCy (SpaCy.io)

Chapter 1: Finding words, phrases, names and concepts
This chapter will introduce you to the basics of text processing with spaCy. You'll learn about the data structures, how to work with trained pipelines, and how to use them to predict linguistic features in your text.

Repulsive Surfaces (Keenan Crane)

Functionals that penalize bending or stretching of a surface play a key role in geometric and scientific computing, but to date have ignored a very basic requirement: in many situations, surfaces must not pass through themselves or each other. This paper develops a numerical framework for optimization of surface geometry while avoiding (self-)collision. The starting point is the tangent-point energy, which effectively pushes apart pairs of points that are close in space but distant along the surface. We develop a discretization of this energy for triangle meshes, and introduce a novel acceleration scheme based on a fractional Sobolev inner product. In contrast to similar schemes developed for curves, we avoid the complexity of building a multiresolution mesh hierarchy by decomposing our preconditioner into two ordinary Poisson equations, plus forward application of a fractional differential operator. We further accelerate this scheme via hierarchical approximation, and describe how to incorporate a variety of constraints (on area, volume, etc.). Finally, we explore how this machinery might be applied to problems in mathematical visualization, geometric modeling, and geometry processing.

The Art of Repair (Traditional Kyoto)

Kintsugi (golden joinery) is the Japanese art of repairing broken pottery with lacquer dusted or mixed with powdered gold, silver, or platinum, a method similar to the maki-e technique. As a philosophy, it treats breakage and repair as part of the history of an object, rather than something to disguise. Lacquerware is a longstanding tradition in Japan, at some point it may have been combined with maki-e as a replacement for other ceramic repair techniques.

More Than You Want to Know About Gift Cards (Kalzumeus)

There are few things comedians and personal finance writers agree on, but one comes up every holiday season: “Gift cards. For when you want to give someone money, except worse.” Like many topics in financial infrastructure, they’re a fascinating Gordian knot of user needs, business incentives, government regulation, and infrastructural weirdness. Let’s start unraveling it.

Spiking Neural Nets (Simons Institute)

n August 2014, a significant advance in computing made the cover of the journal Science. It was IBM’s 5.4 billion-transistor chip that had a million hardware neurons and 256 million synapses. Algorithms running on this “neuromorphic” chip, when fed a video stream, could identify multiple objects, such as people, bicycles, trucks, and buses. Crucially, the hardware neural network consumed a mere 63 milliwatts, about 176,000 times less energy per synaptic event than the same network simulated on a general-purpose microprocessor.

The Invention of Chinese (History Today)

Believing language would unify their struggling nation, Chinese officials began a project to create a national language and define what it meant to speak Chinese.

A Ghostly Galaxy Lacking Dark Matter (ESA Hubble)

NGC 1052-DF2 resides about 65 million light-years away in the NGC 1052 Group, which is dominated by a massive elliptical galaxy called NGC 1052.

The Internet has a Rat Poison Problem (Audobon)

My shopping spree was born out of boredom. On a lazy July morning I was in bed browsing Amazon when I decided to follow up on a tip I had received. I plugged the word “brodifacoum” into Amazon’s search bar, and a second later my screen filled with what are known as second-generation anticoagulant rodenticides, a class of rat poison so dangerous to humans and wildlife that the Environmental Protection Agency strove to keep them from being sold in consumer stores. After clicking around for a few bewildered minutes, I ordered something called Motomco D 31402 Jaguar Rodenticide Pail Pest Control. It cost \$69.99, its delivery was free, and it had a 4.8-star rating. The top customer review said, “Kills them all, but the dead mice smells is not what I need,” which sounded like a solid testimonial.

log4j: between a rock and hard place\

This is making the rounds because highly-profitable companies are using infrastructure they do not pay for. That is a worthy topic, but not the most interesting thing in this particular case because it would not clearly have contributed to preventing this bug. It is the second statement in this tweet that is worthy of attention: the maintainers of log4j would have loved to remove this bad feature long ago, but could not because of the backwards compatibility promises they are held to.

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LLM - large language model) survey - ArXiV
categories:
tags: arxiv  deep-learning  llms  nlp
date: 19 Apr 2023
slug:llms

Signaling
categories:
tags: behavior  signaling
date: 26 Apr 2023
slug:signaling
Proof of X   (julian.digital)
Signaling as a Service   (julian.digital)

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Whistleblowing
categories:
tags: behavior  courage
date: 30 Apr 2023
slug:whistleblowing

Langchain tools
categories:
tags: langchain  llms
date: 19 May 2023
slug:langchain

Auction Theory - Jonathan Levin paper
categories:
tags: auctions  game-theory  pricing
date: 24 May 2023
slug:auction-theory

Auction Theory
categories:
tags: auctions  game-theory  pricing
date: 24 May 2023
slug:auctions

Rails framework principles
categories:
tags: rubyonrails
date: 05 Jun 2023
slug:rails

Transformer models - intro and catalog
categories:
tags: arxiv  deep-learning  llms  transformers
date: 08 Jun 2023
slug:transformers