Update README.md

README.md

@@ -24,9 +24,9 @@ or add the dependency directly in your **cargo.toml** file
 [dependencies]
 unda = "{version}"
 ```
+
 ## Usage
 
-### Dense Network
 ```rust
 use unda::core::network::Network;
 use unda::core::layer::{methods::activations::Activations, layers::{LayerTypes, InputTypes}};
@@ -56,6 +56,25 @@ fn main() {
 }
 ```
 
+
+## Examples
+The unda repository hosts a plethora of example ML models to compute a series of common problems. These examples can be found in the ```/examples``` folder and can be run by entering:
+
+```
+cargo run --release --example {example_name}
+```
+where ```example_name``` is the name of the file/folder you wish to run, omitting the .rs
+
+### Currently, Unda has example implementations for XoR, MNIST and a [breast cancer model from Kaggle](https://www.kaggle.com/datasets/yasserh/breast-cancer-dataset)
+
+**Important**! When using running the MNIST example, please make sure to put the appropriate ubyte files into the /src/util/mnist directory of this repository. We are currently working on using reqwest to automatically build the dataset, but for now it must be done manually
+
+Here are google drive links to the necessary ubyte files
+- [labels](https://drive.google.com/file/d/191BR4awTN-XvIISPeB4_zaHJZ0EgC4-o/view?usp=drive_link)
+- [images](https://drive.google.com/file/d/1vsltbfn7D3ZYFmAhN2fexomUaqr5oG6P/view?usp=drive_link)
+
+## Implications for the future of ML
+
 Using the built in **Input** trait, practically any data type can be mapped to an input for a neural network without the need for cutting corners, and the inner trait for layers allows for a plug and play style to neural network development. Currently, Unda has full support for Dense layers, Adam Optimization for Backprop, Activation functions (Sigmoid, TanH, ReLU and LeakyReLU), and even loss analysis per model and per layer. 
 
 Gradient descent currently can happen both syncronously as stochastic gradient descent or asynchronously through minibatch gradient descent.