##### Part Two: Introduction to Artificial Neural Networks

We will discuss how biology and neurology inspired the field of deep learning. We then discuss artificial neurons, how they are different from their biological cousins, and how they can be tied together to form a neural network. You will learn the specifics of how the neural network learns through backpropagation and gradient descent. This part of the course is aimed to build your intuition as to how an ANN works, while avoiding the mathematical side.

##### Part Two: Readings

###### This week please read either of the following (preferably the first):

######

##### 3 - Successes and Biological Motivation

##### 4 - Artificial Neurons

##### Programming Exercise

##### Forward Pass

This is the first programming part in the course. The idea is for you to translate what you learned in the lecture into code and to work with "activation functions", which we will see a lot of soon.

You need to download the files from the Google Drive below and make sure that you place them all in the same folder.

You only need to open the "Exercises" workbook, unless you get stuck, then the "Solutions" workbook will give you the answers. You will come across an exercise in the workbook in which you have to write the code for various activation functions. This may require you to look online to find the relevant equations, but this should be simple for you to do.

##### 5 - Neural Networks and Backpropagation

##### Video processing: Will be up in 24h

##### 6 - Activation and Loss Functions

##### 7) Sumary of an Introduction to ANNs

##### End of Part Two

Congratulations for completing part two! You can now move on to Part Three where we will start building some models