Deep Learning Portfolio

This portfolio contains all of the supervised and unsupervised Machine Learning and Deep Learning topics that I've covered.

This README will give brief notes on my understanding of the topic discussed in each section and direct links to each of the Python files that contain both code and inline notes.

Dependencies (Ubuntu)

. python-tk . pip . pandas . numpy . matplotlib . sklearn . scipy . Keras . Tensorflow

Main Sections

1. Machine Learning Basics
2. Supervised Deep Learning
3. Unsupervised Deep Learning

Machine Learning Basics

1. Regression Analysis
2. Classification
3. Clustering
4. Association Learning
5. Reinforcement Learning
6. Natural Language Processing
7. Dimensionality Reduction
8. Model Selection & Boosting

Regression Analysis

Classification

Clustering

Association Learning

Reinforcement Learning

Natural Language Processing

Dimensionality Reduction

Model Selection & Boosting

Supervised Deep Learning

1. Artificial Neural Networks
2. Convolutional Neural Networks
3. Recurrent Neural Networks

Artificial Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Unsupervised Learning

1. Self-Organizing Maps
2. Boltzmann Machines
3. Auto-Encoders

Self-Organizing Maps

--- Currently Studying ---

Boltzmann Machines

--- Future Studies ---

Auto-Encoders

--- Future Studies ---

Disclaimer: This code is only my academic work product as a result of taking courses from the Super Data Science team, and as such is their intellectual property.

IDEs

Spyder

. sudo apt-get install python-pyqt5 python-pyqt5.qtsvg python-pyqt5.qtwebkit . sudo pip install spyder

Jupyter

. sudo apt-get install jupyter