Tagged with technical

Fast.ai Course Part 1: Practical Deep Learning for Coders

Practical Deep Learning for Coders

MUST READ: Clueless Individual Attempts to Explain Basic PyMC3 Model. You Won’t Believe What Happens Next.

Walking through a PyMC3 Model with Tiger.

Domain-Driven Design

Software should be optimized for the domain it is performing in, or else it will turn into an unmaintainable nightmare for both developers and domain experts.

Understanding Pyro’s Model and Guide: A Love Story

Introduction to basic Pyro concepts

China’s Social Credit System: A Step Towards Dystopia? Part One: Introduction

Introduction to Social Credit System blog post series

The Dream Machine

How the modern computer came to be, courtesy of the visionary JCR Licklider. Incredible. None of this was an accident.

China’s Social Credit System: A Step Towards Dystopia? Part Two: Historical Context

Historical context behind the SCS

Machine Learning Guide

30 Episodes of Machine Learning content. Best audio resource I've found.

China’s Social Credit System: A Step Towards Dystopia? Part Three: 2014 Social Credit Plan

The culmination of decades of iteration, the 2014 Social Credit Plan

China’s Social Credit System: A Step Towards Dystopia? Part Four: Joint Punishment

2016 plan to enforce and discipline within the social credit system.

Using Artificial Intelligence to Augment Human Intelligence

AIA > AI or IA. Another great piece from Distill.

China’s Social Credit System: A Step Towards Dystopia? Part Six: Looking Ahead

What's next for the SCS?

China’s Social Credit System: A Step Towards Dystopia? Part Five: Barriers

Obstacles in place to the further implementation of the SCS.

Machine Learning Engineering in Ten Parts

Machine Learning Engineering in Ten Parts, with Paper Club

Computer Architecture and the Hardware/Software Interface

Lecture notes from Bradfield's architecture class

Augmenting Long-term Memory

The paper that got me into Anki and spaced repetition. Has 10x'd my studying efficiency and retention.

Languages, Compilers and Interpreters

Lecture notes from Bradfield's languages class

Computer Networking

Lecture notes from Bradfield's networking class

Analyzing 50k fonts using deep neural networks

Perfect dataset for training fonts, trained model even has 40 latent factors.

Startup School 2018

Startup School: How to start a startup. Talks from big YC founders and partners.

Problem Solving with Algorithms and Data Structures

Lecture notes from Bradfield's algorithms class

Systems and Software for Machine Learning at Scale with Jeff Dean - TWiML Talk #121

Good interview with one of the AI Gods.

How to deliver on Machine Learning projects

Step-by-step pipeline from idea to production for an ML project


Lecture notes from Bradfield's databases class

Program Interfaces, Patterns, and Anti-Patterns

Lecture notes from Bradfield's APIs class

Distributed Systems

Lecture notes from Bradfield's distributed systems class

Artificial Intelligence: The Revolution Hasn't Happened Yet

Human-imitative AI is not a good way to frame current progress in AI. Intelligence Augmentation and Intelligence Infrastructure present an equally important (and as-yet-unnamed) class of problems.

Mathematics for Computing

Lecture notes from Bradfield's math class

101: Ben Orenstein - How to Build an App in a Week

Be ruthless about cutting and be flexible about scope. Set hard deadlines.

Operating Systems

Lecture notes from Bradfield's operating systems class

What Truly Makes a Senior Developer

Senior developers understand that nothing is without possible issues, downsides, and risks.

Documents OCR: Improving Efficiency by Making PDFs Searchable

Use Google Cloud Vision. Good OCR pipeline reference for startup.

Part 4: Types of Accounts

Types of Financial Accounts in PM Accounting

Part 2: Double-Entry Bookkeeping

Double Entry Bookkeeping in PM Accounting

Web Architecture 101

Walkthrough of Web Application Architecture

How To Set Up Google Colab/Colaboratory For Building Pyro Models

Setting up Colab to run Pyro models

Reality Driven Development

Kanban + light pairing will give you a flexible, reality driven approach to achieving the real goal that's driving your business: delivering as much value as possible as fast as possible.

Who is MiningLamp? Why was it able to win Tencent's high-value investment?

MiningLamp has gotten significant attention and investment for its police-assisting AI.

Winograd Schema Challenge

The Winograd Schema Challenge aims to address the flaws in the Turing Test to determine if an AI is "human level".

Markov Decision Processes

Markov Decision Processes are Finite State Machines with four key components: state, action, transition function, reward function. They run into the curse of dimensionality.

Designing Very Large (JavaScript) Applications

Lots of good architecture advice (not all JS-specific!) from a JS architect at Google

How I Learned to Stop Worrying and Love the State Machine

Any reasonably complex domain object degenerates into a state machine, so you might as well get ahead of the curve.

Code Smells: Mutation

Make things immutable as much as you can!

Code Smells: Multi Responsibility Methods

Methods should do one thing. Extract things until this is the case.

Code Smells: Null

All about the Null code smell. Use `Optional`, `@NotNull` to combat it.

Fellow Engineers: This is where your money comes from.

Your customers make you money, so if you care about money care about your customers.

Variational Autoencoders Explained

Variational autoencoders are generative encoder-decoder networks with a constraint on the encoding network. Well-explained.

Approaching (Almost) Any Machine Learning Problem

Mental model for approaching ML problems. Very good breakdown.

Effective Java, 2nd Edition

Very good grab bag of tips and tricks for Java. Many were immediately applicable. Explained at the perfect level of abstraction for me.

AI Index Thoughts

Thoughts on the AI Index

AI Index Index

Summarizing a summary of the state of AI

From Research To Practice

Notes on Best Practices for Applying Deep Learning to Novel Applications

Language Modeling Survey

Notes on Exploring the Limits of Language Modeling

Remarques sur la traduction de la machine neurale en apprenant ensemble à aligner et à traduire

Notes on Neural Machine Translation by Jointly Learning to Align and Translate

Paper Notes Template

How we Read Papers


Notes on Bag of Tricks for Efficient Text Classification

GRUs vs. LSTMs

Notes on Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

The Pragmatic Programmer

Collection of somewhat common sense but well-stated programming advice.

Patterns of Enterprise Application Architecture

Solid set of backbone concepts for enterprise applications. Many of his ideas have developed a lot since this book was released in 2002 but it strengthened my understanding to see them explained firsthand.

Reinforcement Learning

Reinforcement learning is a type of unsupervised state-action-transition-reward training mainly used to learn games right now.

Reproducibility and the Philosophy of Data with Clare Gollnick - TWiML Talk #121

Studies are not reproducible and it's not a good look for the industry. Solution: do real research, don't just throw stuff at a wall and see what sticks.

Optimal decision-making with POMDPs

POMDPs are Markov decision processes that have to deal with a partially obervable game.

Bayesian Methods For Hackers

The simplest explanation of Bayesian methods and probabilistic programming I've come across. Says a lot about the field that this book was still extremely difficult to get through.

Newcastle University MAS2317/3317: Introduction to Bayesian Statistics

Notes taken from lecture notes from Newcastle University Bayesian Statistics course, with James from Paper Club

False Discovery Rates

How to reduce the noise of making multiple comparisons.

Being Bayesian

Be more rigorous in real life by thinking in terms of priors, posteriors, and updates.

From Research to Startup, There and Back Again

Decades of experience in Silicon Valley with John Hennessy (current chairman of Alphabet)

Some Code I Deleted

Always look for existing well-written, well-tested code before embarking on your own adventure. No code is better than no code.

Code Smells: Too Many Problems

Break the method into smaller pieces. Work on one smell at a time. Step back, get a bigger picture to model the problem. Introduce new domain objects if appropriate. Document your changes.

Code Smells: If Statements

Avoid crazy conditionals by moving them to the correct place, collapsing them, extracting to methods

Code Smells: Deeply Nested Code

Deeply nested code is bad. Encapsulate, use streams.

Google Rules of Machine Learning

Bite-size, Google-scale advice for ML.

The UX of AI

Machine learning should be human-centered

The cold start problem: how to build your machine learning portfolio

Build a project with an interesting dataset that took obvious effort to collect, and make it as visually impactful as possible.

On Being an Engineering Manager

Grab bag of wisdom on engineering management

AWS, MongoDB, and the Economic Realities of Open Source

Open Source financials echo those of the music industry; what's being sold is not the software, but the packaging. This is dangerous if left unaddressed.

Core Java Volume 1 -- Fundamentals

More of a reference book than a read-through book. Goes through the Java core API. Already knew most of it

Senior Engineers Reduce Risk

The impact of senior engineers goes beyond code.

Kalman Filters

Kalman filters are a simple but powerful technique for determining the most likely current state of an object in motion.

What Tech Stacks are Indie Hackers Using for Their Apps, and Why?

Descriptions of tech stacks and justifications for them at small startups. Bottom line: build modularly with technology you're familiar with.

Transfer Learning

Transfer learning is powerful and underutilized.

Crafting Beautiful UX with API Requests

Developer experience matters internally and externally and there are some simple patterns to make it better.

Beyong Zero-Sum Thinking in the Game of Tech...and Life

Zero sum thinking in politics and economics is outdated and poisonous.

Part 5: Rent Charges and Total Management

Rent charges in PM accounting

Bayesian Machine Learning

Intro to Bayesian Machine Learning

Game Theory

Game theory is the study of equilibria-based solutions.

Online Property Management Software vs. Excel

Use Online Property Management Software for Auditing and Intuition

Income and expenses: What property managers need to know

Summary of the relevant PM income and expense types, and who is responsible for each

Insurance Claim Bookkeeping and Accounting for Real Estate Total Management

How to handle insurance claims in PM software.

Part 1: Intro

PM Accounting

Part 3: Debits and Credits

Debits and Credits in PM Accounting

Memorizing a programming language using spaced repetition software

Programming-specific SRS tips

How to unit test machine learning code

Actual code examples for testing neural networks and ML algorithms yay

Tools for Remote Software Development and Pair Programming

Tools for Remote Software Development and Pair Programming

Operations for Software Developers For Beginners

Ops is worth learning.

Software Estimation: Demystifying The Black Art

More of a reference book than a read-through book. Goes through a lot of software estimation techniques. Meat of the book was in the first section.