engineersCode/EngComp6_deeplearning
A step-by-step introduction to deep learning (a.k.a. neural network) models for scientists and engineers.
This module provides a step-by-step introduction to deep learning and neural networks using Python and Jupyter notebooks. It helps scientists and engineers understand how to build models from data, taking numerical data as input to produce classifications or predictions. The intended user is a scientist or engineer with a background in calculus and linear algebra who wants to learn practical machine learning.
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Use this if you are a scientist or engineer who wants a foundational, hands-on understanding of deep learning concepts like linear and logistic regression, and how to apply them to real-world data using Python.
Not ideal if you are looking for advanced deep learning topics, a high-level overview without coding, or if you do not have a background in calculus and linear algebra.
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12
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3
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Aug 28, 2022
Commits (30d)
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