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.

## Download

Download the PDF here:

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.

**Download Details:**

**Author**: dformoso

**Official Github**: https://github.com/dformoso/deeplearning-mindmap

**License**: Apache-2.0 license

#data #data-analysis #data-science #deep-learning