The announcement says "Our board members include Jack Dorsey, a founder of Twitter, Jeremie Miller, the inventor of Jabber/XMPP, and Jay Graber, CEO of the Bluesky company", which suggests there may be more…?
INNOVATION
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Speaker at Machine Learning Prague Conference on LLM-Driven Games
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I am excited to share that I have been invited to speak at the Machine Learning Prague conference this year. It is a privilege to have the opportunity to discuss my experience in machine learning alongside other knowledgeable experts. My talk will focus on LLM-driven game
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AI Industry Race: Weekly Model Announcements Growing Token Counts
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twitter these days: announcement: we launched a model trained to 100B tokens!
2nd week announcement: we launch a model trained to 200B tokens
3rd week announcement: we just launch a model at 300B tokens. might as well be twitter-board (not tensorboard). lol. -
Spotify Opens Developer Access to Music Platform API
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https://
developer.spotify.com/documentation/
open-access
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Academic Funding for AI Conference Travel: Value Assessment
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As a student, your advisor should be the one funding conference travel to "meaningful" venues. As an advisor, I don't think I would fund travel to an event like this for-profit industry I was invited to (not worth their time or the money).
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Early Access to Mathematics of Machine Learning Book Chapters
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Get early access to the chapters in this book as they are written by the author: "The #Mathematics of #MachineLearning" at https://
tivadardanka.com/books/mathemat
ics-of-machine-learning
… by @TivadarDanka —————
#AI #DeepLearning #BigData #DataScience #LinearAlgebra #Statistics #Calculus #NeuralNetworks #Optimization #ML -
Machine Learning with PyTorch and Scikit-Learn Book Released
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Massive 774-page book by @rasbt “#MachineLearning with #PyTorch and Scikit-Learn: Develop #ML and #DeepLearning models with #Python” at http://
amzn.to/3J6DK4u ————
#100DaysOfCode #BigData #DataScience #AI #NeuralNetworks #DataScientists @PacktPublishing @PacktAuthors -
Autoscaling compute clusters for parallel job execution
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5) Enabling autoscaling of the compute cluster to execute more jobs in parallel during periods of high activity and save cost during periods of low activity.
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Ray Parallelization and Job Checkpointing for Distributed AI
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3) Leveraging Ray to parallelize work at large scale across multiple worker pods in the cluster to achieve performance benchmarks
4) Implementing job checkpointing ensures that jobs always run to completion and users see minimal interruption. -
Kubernetes Lessons Learned Running ML at Snorkel
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Snorkel tackles heavy ML workloads for some of the world's largest organizations. We have built our infrastructure on Kubernetes—even though it wasn't designed for ML! Here are a few lessons learned from @wheeliamhuang
, Tech Lead Manager at Snorkel https://
snorkel.ai/kubernetes-les
sons-learned-at-snorkel-ai/
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