SLC Artificial Neural Network Training

This repository contains the materials delivered in the Artificial Neural Network training held in Software Laboratory Center (SLC), Binus University on August 27-29, 2018.

The codes are separated for each session, with materials as follow:

1. Session 1: Introduction to Python, Perceptron, and Least Mean Square
   • Introduction to Python
   • Perceptron
   • Least Mean Square
2. Session 2.1: Multilayer Perceptron
   • Multilayer Perceptron
   • Tensorboard
   • Multilayer Perceptron Tuning
3. Session 2.2: Recurrent Neural Network
   • Recurrent Neural Network
   • Recurrent Neural Network Tuning
4. Session 3.1: Self-Organizing Map and Principal Component Analysis
   • Self-Organizing Map
   • Principal Component Analysis (to be added in this repository)
   • Eigenface using PCA (to be added in this repository)
5. Session 3.2: Convolutional Neural Network
   • Convolutional Neural Network

All codes are written in Python 3.6 with NumPy and TensorFlow 1.10

Later version of TensorFlow may introduce compatibility issues with these codes