https://github.com/dformoso/deeplearning-mindmap
A mindmap summarising Deep Learning concepts.
https://github.com/dformoso/deeplearning-mindmap
cheatsheet data deep jupyter learning mindmap python science
Last synced: 23 days ago
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A mindmap summarising Deep Learning concepts.
- Host: GitHub
- URL: https://github.com/dformoso/deeplearning-mindmap
- Owner: dformoso
- License: apache-2.0
- Created: 2017-08-18T03:24:56.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-05-14T09:26:58.000Z (about 6 years ago)
- Last Synced: 2025-04-08T15:11:52.908Z (2 months ago)
- Topics: cheatsheet, data, deep, jupyter, learning, mindmap, python, science
- Size: 14 MB
- Stars: 1,673
- Watchers: 90
- Forks: 358
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Learning Mindmap / Cheatsheet - BETA
A Mindmap summarising Deep Learning concepts, Architectures, and the Tensorflow library.
## Overview
Deep Learning is part of a broader family of Machine Learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised, or unsupervised. This is an attempt to summarize this large field in one .PDF file.
## Mindmap on Data Science
Here's another mindmap which focuses on Machine Learning basics and Data Science.
- https://github.com/dformoso/machine-learning-mindmap
## Download
Download the PDF here:
- https://github.com/dformoso/deeplearning-mindmap/blob/master/Deep%20Learning.pdf
I've built the mindmap with MindNode for the Mac. https://mindnode.com
## 1. Concepts
A partial list of the building blocks of Deep Learning architectures, with notes on the mathematics behind each component.
## 2. Architectures
Different Deep Learning architectures have been developed depending on the question being answered. Here's a list of some of them and notes on tuning.
## 3. Tensorflow
TensorFlow is an open source software library for numerical computation using data flow graphs. The mindmap lists some of its components, packages, and overall architecture.
## References
I'm planning to built a more complete list of references in the future. For now, these are some of the sources I've used to create this Mindmap.
- Stanford and Oxford Lectures. CS20SI, CS224d.
- Books:
- Deep Learning - Goodfellow.
- Pattern Recognition and Machine Learning - Bishop.
- The Elements of Statistical Learning - Hastie.
- Colah's Blog. http://colah.github.io
- Kaggle Notebooks.
- Tensorflow Documentation pages.
- Google Cloud Data Engineer certification materials.
- Multiple Wikipedia articles.
## About Me
Twitter:
- https://twitter.com/danielmartinezf
Linkedin:
- https://www.linkedin.com/in/danielmartinezformoso/
Email:
- [email protected]