README.md

   1 # Resources
   2
   3 Basics:
   4  - [Learn numpy](https://numpy.org/learn/)
   5  - [MNIST database of handwritten digits](http://yann.lecun.com/exdb/mnist/)
   6  - [Neuron](https://en.wikipedia.org/wiki/Artificial_neuron)
   7  - [Perceptron](https://en.wikipedia.org/wiki/Perceptron)
   8  - [3Blue1Brown video series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
   9
  10 Too high-level for first-time learning, but apparently very abstract and powerful for real-life:
  11  - [keras](https://keras.io/)
  12  - [tutorial how to recognize handwriting with keras/tensorflow](https://data-flair.training/blogs/python-deep-learning-project-handwritten-digit-recognition/)
  13
  14 # Dependencies
  15
  16     sudo dnf install -y python3-numpy python3-matplotlib
  17
  18 # Steps
  19
  20  - Do the [NumPy quickstart tutorial](https://numpy.org/devdocs/user/quickstart.html); example:
  21
  22 ```py
  23 import numpy as np
  24 import matplotlib.pyplot as plt
  25 grad = np.linspace(0,1,10000).reshape(100,100)
  26 plt.imshow(grad, cmap='gray')
  27 plt.show()
  28
  29 plt.imshow(np.sin(np.linspace(0,10000,10000)).reshape(100,100) ** 2, cmap='gray')
  30 # non-blocking does not work with QT_QPA_PLATFORM=wayland
  31 plt.show(block=False)
  32 plt.close()
  33 ```
  34
  35  - Get the handwritten digits training data with `./download-mnist.sh`
  36
  37  - Read the MNIST database into numpy arrays with `./read_display_mnist.py`. Plot the first ten images and show their labels, to make sure the data makes sense:
  38
  39    ![visualize training data](screenshots/mnist-visualize-training-data.png)