Machine learning in Elixir : learning to learn with Nx and Axon / Sean Moriarity.

Format:

Book

Author/Creator:

Moriarity, Sean, author.

Language:

English

Subjects (All):

Machine learning.

Elixir (Computer program language).

Physical Description:

1 online resource (359 pages)

Edition:

First edition.

Place of Publication:

[Raleigh, North Carolina] : The Pragmatic Programmers, LLC, [2024]

Summary:

Machine Learning in Elixir, authored by Sean Moriarity, explores the integration of machine learning capabilities within the Elixir programming language using the Nx ecosystem. The book provides a comprehensive guide for Elixir programmers to develop machine learning models and applications, covering foundational concepts, deep learning techniques, and practical implementation strategies. It aims to equip developers with the skills needed to use Elixir for machine learning tasks, traditionally dominated by languages like Python. The book also highlights the advantages of functional programming in machine learning and offers practical examples and tools to facilitate learning. It is intended for software developers and those interested in exploring machine learning through the lens of Elixir. Generated by AI.

Contents:

Cover

Table of Contents

Disclaimer

Acknowledgments

Preface

Why Elixir for Machine Learning?

Who This Book Is For

What's in This Book

How to Use This Book

Part I-Foundations of Machine Learning

1. Make Machines That Learn

Classifying Flowers

Learning with Elixir

Wrapping Up

2. Get Comfortable with Nx

Thinking in Tensors

Using Nx Operations

Representing the World

Going from def to defn

3. Harness the Power of Math

Understanding Machine Learning Math

Speaking the Language of Data

Thinking Probabilistically

Tracking Change

4. Optimize Everything

Learning with Optimization

Regularizing to Generalize

Descending Gradients

Peering into the Black Box

5. Traditional Machine Learning

Learning Linearly

Learning from Your Surroundings

Using Clustering

Making Decisions

Part II-Deep Learning

6. Go Deep with Axon

Understanding the Need for Deep Learning

Breaking Down a Neural Network

Creating Neural Networks with Axon

7. Learn to See

Identifying Cats and Dogs

Introducing Convolutional Neural Networks

Improving the Training Process

Going Beyond Image Classification

8. Stop Reinventing the Wheel

Identifying Cats and Dogs Again

Fine-Tuning Your Model

Understanding Transfer Learning

Taking Advantage of the Machine Learning Ecosystem

9. Understand Text

Classifying Movie Reviews

Introducing Recurrent Neural Networks

Understanding Recurrent Neural Networks

10. Forecast the Future

Predicting Stock Prices

Using CNNs for Single-Step Prediction

Using RNNs for Time-Series Prediction

Tempering Expectations

11. Model Everything with Transformers

Paying Attention.

Going from RNNs to Transformers

Using Transformers with Bumblebee

12. Learn Without Supervision

Compressing Data with Autoencoders

Learning a Structured Latent

Generating with GANs

Learning Without Supervision in Practice

Part III-Machine Learning in Practice

13. Put Machine Learning into Practice

Deciding to Use Machine Learning

Setting Up the Application

Integrating Nx with Phoenix

Seeding Your Databases

Building the Search LiveView

14. That's a Wrap

Learning from Experience

Diffusing Innovation

Talking to Large Language Models

Compressing Knowledge

Moving Forward

Bibliography

Index

- DIGITS -

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- H -

- I -

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- L -

- M -

- N -

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- Q -

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- S -

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- U -

- V -

- W -

- X -

- Y -

- Z -.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

Description based on print record.

Includes bibliographical references.

ISBN:

9798888651261

9798888651278

OCLC:

1456760717