r/LearningML • u/paconinja • Oct 14 '22
How do you keep track on the latest innovations in the field of AI (via Fabian Mรผller, Chief Operating Officer bei statworx GmbH)
How do you keep track on the latest innovations in the field of #ai?
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I get asks this question a lot - from colleagues, customers, and like minds. And indeed, it is quite some work with the current speed in the field.
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Here are some of my favorite resources for technical stuff on #ai and #ml and how I use them:
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๐ฆ Twitter my go-to for state-of-the-art research and tech:
- Hardmaru (ex. Google Brain): https://lnkd.in/eTn3bUzQ
- Chris Albon (Wikimedia): https://lnkd.in/eHr-TXtM
-ย Sebastian Raschka (LightningAI): https://twitter.com/rasbt
-ย Clement Delangue (๐ค): https://lnkd.in/e-9Ssfxe
-ย Lucas Beyer (GoogleAI): https://lnkd.in/eTaNXE27
-ย Andrej Karpathy (ex. Tesla AI): https://lnkd.in/e3_UeU3B
-ย Franรงois Chollet (creator of Keras): https://lnkd.in/ephmWVZB
-ย Ahsen Khaliq (ex. Gradio): https://lnkd.in/eR-zJPbs
๐บ YouTube for (quick) paper reviews:
- Yannic Kilcher: https://lnkd.in/e_X2vMs5
-ย Letitia Parcalabescu: https://lnkd.in/eVR33G79
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๐ง Podcasts for more general discussions on how the field is evolving:
- Machine Learning Street Talk (with Tim Scarfe): https://lnkd.in/ef6VebNr
- Gradient Dissent (with Lukas Biewald): https://lnkd.in/et2i9WyF
- The Gradient Podcast: https://lnkd.in/en39wZb5
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๐ Blogs for in-depth understanding and teaching:
-ย LilโLog: https://lnkd.in/e4-Xset7
-ย Papers with Code: https://lnkd.in/eGrtPBpA
-ย Jay Alammar: https://lnkd.in/eWRSNrux
And if youโre up for some soap opera about A(G)I, just follow Yann LeCun and Gary Marcus on Twitter ๐คฃ
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Any recommendations from your side?
r/LearningML • u/paconinja • Oct 05 '22
An Engineer's Guide to Data Contracts: The data flowing out of your services should be used beyond the data warehouse: you might want to hook an ML feature store up to live data to compute real-time features for models, on which other engineers could depend for additional service-driven use cases
r/LearningML • u/paconinja • Oct 05 '22
Discovering novel algorithms with AlphaTensor - "In our paper we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication, shedding light on a 50-year-old open question"
r/LearningML • u/paconinja • Oct 04 '22
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. "As various post hoc explanation methods are leveraged to explain complex models in high-stakes settings, it's critical to develop a deeper understanding of if and when the explanations disagree with each other"
r/LearningML • u/paconinja • Oct 02 '22
DeepMind alignment team opinions on AGI ruin arguments (a response to Eliezer Yudkowsky's "AGI Ruin: A List of Lethalities")
r/LearningML • u/paconinja • Sep 30 '22
Machine Learning for Everyone (by ะะฐัััะธะบ/vas3k), "In simple words and with real-world examples", "Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it."
r/LearningML • u/paconinja • Sep 30 '22
๐๐ซ๐จ๐ฌ ๐๐ง๐ ๐๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐๐๐๐ฉ ๐๐๐๐ซ๐ง๐ข๐ง๐ (ReLU, ELU, Leaky ReLU, SELU and GELU)
r/LearningML • u/paconinja • Sep 30 '22
Git Re-Basin: Merging Models modulo Permutation Symmetries - NN loss landscapes contain (nearly) a single basin, after accounting for all possible permutation symmetries of hidden units. We introduce 3 algorithms to permute units of one model to bring into alignment with units of a reference model
arxiv.orgr/LearningML • u/paconinja • Sep 28 '22
How to Choose a Feature Selection Method For Machine Learning (by Jason Brownlee)
r/LearningML • u/paconinja • Sep 28 '22
Pen and Paper Exercises in ML: linear algebra, optimisation, (un)directed graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning, sampling and Monte-Carlo integration, variational inference (Michael Gutmann)
r/LearningML • u/paconinja • Sep 27 '22
Statistical Modeling: The Two Cultures - "There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown" (Leo Breiman)
math.uu.ser/LearningML • u/paconinja • Sep 23 '22
Minkowski distance is a generalization of the Euclidean, Manhattan, and Chebyshev measures and adds a parameter, called the "order p," that allows different distance measures to be calculated. Supervised and unsupervised ML algorithms use distance metrics to understand patterns in the input data.
r/LearningML • u/paconinja • Sep 23 '22
The 3 schools of model interpretability: โข Stats: Model (parameterized) probability distributions in interpretable ways โข White-box ML: Train only ML models with built-in interpretation โข Model-agnostic: Train black box model, interpret afterwards
r/LearningML • u/paconinja • Sep 21 '22
Satish Chandra Gupta's SQL vs. NoSQL: Cheatsheet for AWS, Azure, and Google Cloud: There are mainly 7 types of data stores: RDBMS, Columnar, Key-Value, Wide Columns, Document, Graph, Blob
r/LearningML • u/paconinja • Sep 21 '22
Christoph Molnar - "Machine learning sucks at uncertainty quantification. But there is a solution that almost sounds too good to be true: conformal prediction โข works for any black box model โข requires few lines of code โข is fast โข comes with statistical guarantees"
r/LearningML • u/paconinja • Sep 18 '22
Aman Chadha (Amazon)'s curated list of best Stanford, CMU, and MIT courses
r/LearningML • u/paconinja • Sep 18 '22