Tagged with ml

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

Practical Deep Learning for Coders

Machine Learning Guide

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

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

Walking through a PyMC3 Model with Tiger.

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

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

Historical context behind the SCS

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.

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

Obstacles in place to the further implementation of the SCS.

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

What's next for the SCS?

Using Artificial Intelligence to Augment Human Intelligence

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

How to deliver on Machine Learning projects

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

Analyzing 50k fonts using deep neural networks

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

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

Good interview with one of the AI Gods.

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.

#47 - Catherine Olsson & Daniel Ziegler on the fast path into high-impact ML roles

OpenAI and Google Brain engineers on their unconventional paths into AI and the impactful work they're doing

Local Governments Power Up to Advance China's National AI Agenda

China local governments propose aggressive plans totaling $400bn in AI money by 2030!

Documents OCR: Improving Efficiency by Making PDFs Searchable

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

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.

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

Setting up Colab to run Pyro models

Ash Fontana -- Investing in Artificial Intelligence

A top AI investor gives his perspective on the industry and his investment thesis

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

Jeremiah Lowin - Machine Learning in Investing

Investment theses of a top ML investor.

Paper Notes Template

How we Read Papers

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".

GRUs vs. LSTMs

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

#102 - Andrew Kortina

Founder of Venmo and Fin. Future of work.

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

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.


Notes on Bag of Tricks for Efficient Text Classification

Transfer Learning

Transfer learning is powerful and underutilized.

From Research to Startup, There and Back Again

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

Being Bayesian

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

Google Rules of Machine Learning

Bite-size, Google-scale advice for ML.

Game Theory

Game theory is the study of equilibria-based solutions.

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.

Mr. Robot

Report on Geoff Hinton and his capsule networks

Bayesian Machine Learning

Intro to Bayesian Machine Learning

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.

Kalman Filters

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

False Discovery Rates

How to reduce the noise of making multiple comparisons.

How to unit test machine learning code

Actual code examples for testing neural networks and ML algorithms yay