emilwallner/Deep-Learning-From-Scratch
Six snippets of code that made deep learning what it is today.
This resource provides foundational code examples to understand how deep learning algorithms work from the ground up. It takes mathematical concepts like cost functions and gradient descent, and translates them into simple, executable code snippets. This is ideal for students, educators, or researchers who want to grasp the core mechanics behind deep neural networks.
263 stars. No commits in the last 6 months.
Use this if you want to understand the fundamental mathematical and algorithmic building blocks of deep learning by seeing and running simple, historical code examples.
Not ideal if you are looking for production-ready deep learning frameworks or tools to apply to complex real-world datasets.
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263
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57
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 10, 2019
Commits (30d)
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