Thank you. JAX works at the NumPy level. So we can't actually compare it with Pytorch which is a framework with high-level APIs. Regarding the advantages, have already mentioned them in the thread.
MACHINE LEARNING
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Artificial Intelligence versus Real Intelligence: Innovation and Future of Work
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#FridayFun The #ArtificialIntelligence vs real #intelligence #innovation #tech #CES2023 #AI #ML #metaverse #FutureofWork #Leadership Credits: @PawlowskiMario @Fabriziobustama @enilev @Hana_ElSayyed @JeroenBartelse @labordeolivier @wissen_tech @anand_narang @AkwyZ
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Machine Learning Explainability Through Conversational AI Systems
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Should we care about machine learning model explainability? Can it be done through conversations? Join @hima_lakkaraju and @JayAlammar to learn more about ML explainability and TalkToModel, an interactive dialogue system for explaining ML models! https://
info.cohere.ai/talking-langua
ge-ai-5
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Dimensionality Reduction and Curse of Dimensionality in AI
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In this @Cognilytica #AIToday #podcast AI Glossary Series episode 'Dimension, Curse of Dimensionality, Dimensionality Reduction' hosts @rschmelzer & @kath0134 define these terms & explain how they relate to #AI. Full episode: https://
aidatatoday.com/ai-today-podca
st-ai-glossary-series-dimension-curse-of-dimensionality-dimensionality-reduction/?utm_source=dlvr.it&utm_medium=twitter
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#data #dataanalysis #ML #tech -
Daily Python Data Science Machine Learning Tutorials Wrap
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That's a wrap! Every day, I share tutorials around Python, Data Science & Machine Learning. You can follow me → @Sumanth_077 Like/RT the first tweet to support my work and help this reach more people
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XLA and Automatic Parallelization in JAX for Computation Speed
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XLA & Automatic Parallelization It also supports XLA or Accelerated Linear Algebra, which is an optimizing compiler, designed specifically to increase the computation speed. And also supports Parallelization and can be done using "pmap()" https://
jax.readthedocs.io/en/latest/note
books/quickstart.html
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Automatic Vectorization with JAX vmap Function
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Automatic Vectorization Vectorization helps faster code execution and Jax has an inbuilt function to do the same And can be done with using "vmap()" function Here are the detailed use cases and implementation of Vectorization. http://
jax.readthedocs.io/en/latest/_aut
osummary/jax.vmap.html#jax.vmap
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Auto Differentiation: Automating Derivative Calculation with JAX
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Auto Differentiation This is really powerful to automate the calculation of derivatives especially when differentiating complex algorithms and mathematical functions. It is highly useful in training machine learning models and can be done with 'jax.grad()'
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JAX NumPy: Performing NumPy Operations with JAX Library
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You can perform almost all Numpy Operations with Jax. JAX provides 'jax.numpy' that helps you to perform Almost anything that can be done with 'numpy' Here is how you can use "jax.numpy"