Erik Linder-norén
Staff Machine Learning Engineer at Apple
Stockholm, Stockholm County, Sweden
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Erik Linder-norén is a Staff Machine Learning Engineer at Apple with 11 years of experience building production computer vision and ML systems. He holds an M.Sc. in Machine Learning and AI and studied across Linköping, ETH Zürich, University of Amsterdam and VU Amsterdam, blending strong academic foundations with applied research. At Apple he progressed from engineer to staff, driving models and systems for large-scale visual AI, and earlier roles span research engineering and industry deployments. An active open-source maintainer, his Keras- and PyTorch-based GAN and YOLO implementations and the popular "ML-From-Scratch" repo have attracted wide use (his projects have amassed over 25k GitHub stars). Outside work he channels creativity into side projects and stays engaged with basketball and hands-on building.
11 years of coding experience
11 years of employment as a software developer
Master of Science (M.Sc.) Machine Learning and Artificial Intelligence, Master of Science (M.Sc.) Machine Learning and Artificial Intelligence at Vrije Universiteit Amsterdam (VU Amsterdam)Master of Science (M.Sc.) Machine Learning and Artificial Intelligence, Master of Science (M.Sc.) Machine Learning and Artificial Intelligence at University of AmsterdamStudies in Natural Sciences, Studies in Natural Sciences at University of GävleMaster of Science (M.Sc.) Machine Learning and Artificial Intelligence, Master of Science (M.Sc.) Machine Learning and Artificial Intelligence at Linköping UniversityBachelor of Science (B.Sc.) Computer Science, Bachelor of Science (B.Sc.) Computer Science at ETH Zürich
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Github Skills (23)
pytorch10
convolutional-neural-networks10
python10
machine-learning10
image-translation10
generative-adversarial-network10
linear-regression10
keras10
deep-learning10
image-generation10
trainings10
naive-bayes10
neural-network10
object-detection10
computer-vision10
Programming languages (2)
Github contributions (5)
eriklindernoren/Keras-GAN
Jul 2017 - Jan 2021
Keras implementations of Generative Adversarial Networks.
Role in this project:
ML Engineer
ML EngineerContributions:151 commits, 20 PRs, 125 pushes in 3 years 6 months
Contributions summary:Erik contributed code related to the implementation of different Generative Adversarial Networks (GANs) in Keras. They implemented several GAN architectures, including ACGAN, DCGAN, WGAN, and SGAN, showcasing an understanding of various GAN loss functions and architectures. The user's work also encompassed the implementation of more advanced GAN models such as InfoGAN and BiGAN.
implementationsdeep-learningadversarialneural-networksgan
eriklindernoren/ML-From-Scratch
Feb 2017 - Oct 2019
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Role in this project:Data Scientist
Data ScientistContributions:365 commits, 11 PRs, 322 pushes in 2 years 8 months
Contributions summary:Erik contributed to the implementation of various machine learning models, demonstrating a focus on the project's core goal of building a machine learning library from scratch. They implemented linear regression, k-nearest neighbors, multi-layer perceptrons, and Naive Bayes models. Furthermore, they implemented a decision tree, a random forest, and a Bayesian Regression.
everythingpythondata-miningdeep-reinforcement-learningbare
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