artificial-neural-networks/Readme.md

# Introduction 
The present session will introduce the Python classes and how to create your own Artificial Neural Network (ANN) class. The class will implement the `__init__`,  `feedforward`, and `backpropagation` methods. 

Each method will be described in detail to understand the  ANN’s inner work. Then, the handwriting MNIST dataset will be used to train and test our ANN. 

# Objectives
- Learn the basics of using Python Classes
- Create methods for: create, feedforward, and backpropagation
- Train the ANN
- Test the ANN

# Pending tasks (Your work)

- Create a Python function to evaluate the ANN’s performance by passing all the test images(`mnist_test.csv`) and compare the target with the ANN’s results. 
- Make a plot of the ANN’s evolution performance for at least 10 combinations of the number of hidden nodes, learning rate, and the number of epochs.
-												initial commit

											
										
										
											11 months ago
+								# Introduction
 								The present session will introduce the Python classes and how to create your own Artificial Neural Network (ANN) class. The class will implement the `__init__`,  `feedforward`, and `backpropagation` methods.
 								Each method will be described in detail to understand the  ANN’s inner work. Then, the handwriting MNIST dataset will be used to train and test our ANN.
 								# Objectives
 								- Learn the basics of using Python Classes
 								- Create methods for: create, feedforward, and backpropagation
 								- Train the ANN
 								- Test the ANN
 								# Pending tasks (Your work)
 								- Create a Python function to evaluate the ANN’s performance by passing all the test images(`mnist_test.csv`) and compare the target with the ANN’s results.
 								- Make a plot of the ANN’s evolution performance for at least 10 combinations of the number of hidden nodes, learning rate, and the number of epochs.