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Deep Learning from Scratch: Building Neural Networks with Python

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This document is based on Seth Weidman's book Deep Learning from Scratch, which offers a hands-on, intuitive approach to understanding and implementing deep learning algorithms without relying on high-level libraries like TensorFlow or Keras. It walks readers through the mathematical foundations of neural networks and guides them in building models from the ground up using only Python and NumPy. Key topics covered include: The theory behind neural networks and deep learning Forward and backward propagation Gradient descent and optimization Building multilayer perceptrons (MLPs) from scratch Understanding loss functions and activation functions Training models on real datasets This resource is ideal for learners who want to deeply understand how deep learning works under the hood, and for educators looking to teach the fundamentals in a transparent, code-first way.

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Deep Learning
from Scratch
Building with Python from First Principles




Seth Weidman

,
, Deep Learning from Scratch
Building with Python from First Principles




Seth Weidman




Beijing Boston Farnham Sebastopol Tokyo

, Deep Learning from Scratch
by Seth Weidman
Copyright © 2019 Seth Weidman. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional
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September 2019: First Edition

Revision History for the First Edition
2019-09-06: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781492041412 for release details.

The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Deep Learning from Scratch, the cover
image, and related trade dress are trademarks of O’Reilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publisher’s views.
While the publisher and the author have used good faith efforts to ensure that the information and
instructions contained in this work are accurate, the publisher and the author disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.




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