Machine learning

TODO

Articles

• You Don’t Need Math For Machine Learning

• Machine Learning, Kolmogorov Complexity, and Squishy Bunnies

• Variational Graph Auto-Encoders

• Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

• Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?

• Unsolved Problems in ML Safety

• Data Movement Is All You Need: A Case Study on Optimizing Transformers

• Learning to Superoptimize Real-world Programs

• The First Rule of Machine Learning: Start without Machine Learning

• The Values Encoded in Machine Learning Research

• Multi-Task Learning as Multi-Objective Optimization

• Tutorial: Performance Engineering for Machine Learning and Scientific Computing

• A visual introduction to Gaussian Belief Propagation

• How to avoid machine learning pitfalls: a guide for academic researchers

• A Gentle Introduction To Gradient Descent Procedure

• Algorithmic Concept-based Explainable Reasoning

• In Search of Lost Domain Generalization

• Linear unit-tests for invariance discovery

• Solving Machine Learning Performance Anti-Patterns: a Systematic Approach

• Popular Machine Learning Interview Questions

• Contrastive Representation Learning

• The Scaling Hypothesis

• Towards Causal Representation Learning

• An Attention Free Transformer

• The Modern Mathematics of Deep Learning

• Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

• Informative Dropout for Robust Representation Learning: A Shape-bias Perspective

• E(n) Equivariant Normalizing Flows

• Pay Attention to MLPs

• Differentially Private Learning Needs Better Features (or Much More Data)

• How to Write Design Docs for Machine Learning Systems

• The Mathematical Foundations of Machine Learning

• Joint Universal Syntactic and Semantic Parsing

• GeoGuessing with Deep Learning

• Testing ML Systems: Code, Data and Models

• Free Lunch for Few-shot Learning: Distribution Calibration

• An Inferential Perspective on Federated Learning

• Learning Curve Theory

• 63 Machine Learning Algorithms — Introduction

• Can a neural network train other networks?

• What is concept drift and why is it necessary to detect it?

• Understand how data drift affects AI performance and how you can detect them

Books

• Grokking Machine Learning[0/350]

• Grokking Deep Learning[0/336]

• Distributed Machine Learning Patterns

• Practical Deep Learning for Cloud, Mobile, and Edge

• Machine Learning with Python Cookbook

• Perceptrons: an introduction to computational geometry (Marvin Minsky, Seymour Papert)

Courses

• Machine Learning with Python

• Full Stack Deep Learning - Spring 2021

• Arsenii Ashukha: Ensemble Generation

• MLSys Seminars

• Understanding Deep Learning

• How Machine Language Works

• Optimization Methods for Machine Learning and Engineering (KIT Winter Term 20/21)

• Deep Learning на пальцах - 2019

• 2020 Machine Learning Roadmap (95% valid for 2022)

Links

• Learn Machine Learning

• Learney

• Machine Learning wiki

• Deep Learning wiki

• Дорожная карта математических дисциплин для машинного обучения, часть 1

• Machine Learning Guide (Podcasts)

• while True: learn()

• Learn Machine Learning in 3 Months

• Awesome Machine Learning and AI Courses

• Machine Learning Engineer Roadmap in 2021

• Machine Learning Roadmap

• Best-of Machine Learning with Python

• Machine Learning Collection

• Awesome Normalizing Flows

• ML YouTube Courses

• Machine Learning for Beginners - A Curriculum

• MLOps-Basics

• The Ultimate FREE Machine Learning Study Plan

• Machine Learning Street Talk

• A Curated list of tools for MLOps

• Explaination of common MLOps terms