Add more description to demos.

Author: trekhleb

README.md

@@ -80,7 +80,7 @@ _Usage examples: as a substitute of all other algorithms in general, image recog
 - 📗 [Math | Multilayer Perceptron](homemade/neural_network) - theory and links for further readings
 - ⚙️ [Code | Multilayer Perceptron](homemade/neural_network/multilayer_perceptron.py) - implementation example
 - ▶️ [Demo | Multilayer Perceptron | MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_demo.ipynb) - recognize handwritten digits from `28x28` pixel images.
-- ▶️ [Demo | Multilayer Perceptron | Fashion MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_fashion_demo.ipynb) - recognize the type of clothes from `28x28` pixel images.
+- ▶️ [Demo | Multilayer Perceptron | Fashion MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_fashion_demo.ipynb) - recognize the type of clothes (Dress, Coat, Sandal, etc.) from `28x28` pixel images.
 
 ## Machine Learning Map
 

homemade/neural_network/README.md

@@ -4,7 +4,7 @@
 
 ▶️ [Demo | Multilayer Perceptron | MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_demo.ipynb) - recognize handwritten digits from `28x28` pixel images.
 
-▶️ [Demo | Multilayer Perceptron | Fashion MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_fashion_demo.ipynb) - recognize the type of clothes from `28x28` pixel images.
+▶️ [Demo | Multilayer Perceptron | Fashion MNIST](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/neural_network/multilayer_perceptron_fashion_demo.ipynb) - recognize the type of clothes (Dress, Coat, Sandal, etc.) from `28x28` pixel images.
 
 ## Definition