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"
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JAX vs NumPy: GPU and TPU Support Comparison
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We have Numpy which is the fundamental python library for scientific computing. Why do we need Jax or How it differs from Numpy Numpy is only compatible with the CPU. JAX provides an implementation of NumPy with both GPU and TPU support. https://
github.com/google/jax -
Jax: A Faster Python Library for Machine Learning and Computing
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"Jax" is a python library for numerical computing, machine learning and is really faster than Numpy Here is why you should care about Jax and its really powerful features in Machine Learning:
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ChatGPT Boosts Programming Productivity Tenfold
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¡BUAH! Usar ChatGPT para programar es un boost de rendimiento brutal. Ya no programo, construyo Llegar de la idea al resultado funcional es muchísimo más rápido y con un desgaste mental mucho más bajo. ¿Y vosotros, qué boost de mejora notáis? Yo, fácil, un x10 más rápido.
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Whisper Model Performance Improvements Without Overhead
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Lo que muestran en la tabla es que es el mismo rendimiento que el modelo original. Fíjate el 0.0% rate que indican entre las dos filas de los modelos Whispers. Lo que he probado, cualitativamente obtengo muy buenos resultados. Magia negra.
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Hugging Face Hub: Unlocking AI Datasets and Possibilities
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With Hub, the possibilities are endless and the true potential is just waiting to be unlocked. Start exploring today, one search at a time, and see where it takes you <3 Here's an example to find all the speech recognition datasets for French : https://
huggingface.co/search/full-te
xt?q=speech+recognition+French&type=dataset
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