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Machine Learning in Elixir Learning to Learn with Nx and Axon (True PDF)

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English | October 1, 2024 | ISBN: 9798888650349 | True PDF | 374 pages | 49.7 MB



Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments
in machine learning. Despite the ubiquity of machine learning applications running in production, there are only a few
viable language choices for data science and machine learning tasks. Elixir's Nx project seeks to change that. With Nx,
you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic
language like Elixir. In this book, you'll learn how to leverage Elixir and the Nx ecosystem to solve real-world
problems in computer vision, natural language processing, and more.

The Elixir Nx project aims to make
machine learning possible without the need to leave Elixir for solutions in other languages. And even if concepts like
linear models and logistic regression are new to you, you'll be using them and much more to solve real-world problems in
no time.

Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming
style you're used to and how it enables you to write machine learning algorithms. Use your understanding of this
paradigm to implement foundational machine learning algorithms from scratch. Go deeper and discover the power of deep
learning with Axon. Unlock the power of Elixir and learn how to build and deploy machine learning models and pipelines
anywhere. Learn how to analyze, visualize, and explain your data and models.

Discover how to use machine
learning to solve diverse problems from image recognition to content recommendation - all in your favorite programming
language.

What You Need

You'll need a computer with a working installation of Elixir v1.12 and
Erlang/OTP 24. For some of the more compute intensive examples, you'll want to use EXLA, which currently only supports
x86-64 platforms. While not explicitly required, some examples will demonstrate programs running on accelerators such as
CUDA/ROCm enabled GPUs and Google TPUs. Most of these programs will still run fine on a regular CPU, just for much
longer periods of time.

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