To some extent only. This approach is far from perfect. Here is a (simplified) tutorial from a while back: https://
github.com/hardmaru/slime
volleygym/blob/master/TRAINING.md
…
CODE
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Imperfect approach with simplified tutorial link
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5 Ultimate Skills Every Seasoned Data Scientist Needs
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The 5 Ultimate Skills of a Seasoned Data Scientist https://
catherinescareercorner.com/2021/04/17/the
-5-ultimate-skills-of-a-seasoned-data-scientist/
… #BigData #Analytics #DataScience #AI #IoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #React #DataScientist #Programming #Coding #100DaysofCode #SQL #Excel -
Subscribe to Best Applied Machine Learning Coding Videos
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This guy makes one of the best applied machine learning coding videos: http://
bit.ly/abhitubesub have you subscribed to him yet? -
Linters and Code Best Practices Discussion Thread
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third thread like that in a week aren't there linters for this kind of best practices
it shouldnt even be a discussion (obv never nest if you can avoid it) -
StateOfTheArt Free Conference: Learn LLMs, Agents, Enterprise AI
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Here's an excellent opportunity to learn how to build with LLMs. StateOfTheArt() is a FREE hands-on AI conference hosted by AbacusAI! It covers:
– LLMs
– AI Agents
– Custom LLMs in Enterprise AI Bonus! Insightful talks by @Scobleizer on AI & much more! FREE Registration: -
Multi-backend Keras solves JAX adoption challenges and migration costs
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For instance, JAX has considerable benefits to offer, but so far its adoption has lagged behind — simply because it is quite difficult to adopt, and if you have an existing stack, the cost of migration is heavy. Multi-backend Keras solves this.
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Multi-backend Keras drives innovation without framework monopoly
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As long as there is innovation, there can't be a framework monopoly. I believe multi-backend Keras encourages innovation on the front of low-level frameworks, because it can make new capabilities immediately available to most devs (via a new backend) without requiring a migration
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XLA Compiler Optimizes AI Inference and Training Efficiency
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XLA accelerates both inference and training computations. It can, for example, fuse together many operations to perform them more efficiently and with less memory traffic. It's a compiler and doesn't really care what kind of computation it's being asked to optimize.
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LangSmith Platform Walkthrough Demonstration
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A great walkthrough of LangSmith platform from @Avra_b !
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Modern Columnar Data Format for ML and LLMs in Rust
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Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. https://
bit.ly/3NwSGek #AI #MachineLearning #DeepLearning #LLMs #DataScience