<p align="center"><b>YAIL (Yet another AI Library)</b> ⛵</p>

# Goal
The goal is to provide all beginners (like us) with the resources to build their first neural network from scratch.

# Prerequisites
I'd rather redirect you to some incredible resources that already exist than trying to re-explain everything. If you go through the links you should feel comfortable with the explanations into the code

- 3B1B neural networks series: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi  
- Michael Nielsen's book (best explanations found beginner): http://neuralnetworksanddeeplearning.com/
- Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville: http://www.deeplearningbook.org/

If you are not familiar with multivariable calculus check some videos from one of these series:
- https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7 
- https://www.youtube.com/playlist?list=PL4C4C8A7D06566F38

# Features

| Optimizer     | 
| ------------- |
| stochastic gradient descent |

| Loss     | 
| ------------- |
| mean squared error |
| cross entropy |

| Activation |
| ------------- |
| sigmoid |
| leaky relu |
| `softmax` **todo** |

| Layer     | 
| ------------- |
| flatten |
| dense |
| `conv` **todo** |
| `max pool` **todo** |


# Library 
- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) linear algebra 
- [OpenCV](https://opencv.org/releases.html) quick installation [guide](https://github.com/pascal-canuel/VSOpenCV) on visual studio