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NumPy & JAX NumPy (Part 1)

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In this first video of our two-part series we'll begin a comparison of the standard NumPy library to its counterpart in the JAX ecosystem, jax.numpy. Since you've got experience with NumPy and PyTorch, the goal here is to bridge the gap – showing you how jax.numpy leverages that familiar API but operates quite differently under the hood, and why those differences matter for high-performance computing, especially in machine learning. In this first part we’ll be focusing on the fundamental differences between NumPy and JAX, highlighting the performance gains and design principles that make JAX a compelling choice for high-performance computing.

Resources:
Learn more → https://goo.gle/learning-jax

Subscribe to Google for Developers → https://goo.gle/developers

Speaker: Robert Crowe
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Google, developers, pr_pr: AI DevRel (fka Core ML);
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