AakashKumarNain/TF_JAX_tutorials
All about the fundamental blocks of TF and JAX!
These tutorials help deep learning practitioners understand the core mechanics of TensorFlow and JAX. By providing runnable notebooks that explain concepts like tensors, variables, and automatic differentiation, it helps you build a stronger mental model of how these frameworks operate. This is for anyone from new to experienced machine learning engineers, data scientists, and researchers looking to deepen their foundational knowledge.
279 stars. No commits in the last 6 months.
Use this if you are a machine learning developer or researcher seeking to understand the underlying principles and "how-it-works" of TensorFlow and JAX, rather than just using them as black boxes.
Not ideal if you are looking for an official documentation replacement or a cookbook of ready-to-use solutions for specific deep learning problems.
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279
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25
Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 04, 2021
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