README.md: First steps and resources
authorMartin Pitt <martin@piware.de>
Fri, 28 Aug 2020 05:37:04 +0000 (07:37 +0200)
committerMartin Pitt <martin@piware.de>
Sun, 30 Aug 2020 09:20:52 +0000 (11:20 +0200)
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+# Resources
+
+Basics:
+ - [Learn numpy](https://numpy.org/learn/)
+ - [MNIST database of handwritten digits](http://yann.lecun.com/exdb/mnist/)
+ - [Neuron](https://en.wikipedia.org/wiki/Artificial_neuron)
+ - [Perceptron](https://en.wikipedia.org/wiki/Perceptron)
+ - [3Blue1Brown video series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
+
+Too high-level for first-time learning, but apparently very abstract and powerful for real-life:
+ - [keras](https://keras.io/)
+ - [tutorial how to recognize handwriting with keras/tensorflow](https://data-flair.training/blogs/python-deep-learning-project-handwritten-digit-recognition/)
+
+# Dependencies
+
+    sudo dnf install -y python3-numpy python3-matplotlib
+
+# Steps
+
+ - Do the [NumPy quickstart tutorial](https://numpy.org/devdocs/user/quickstart.html); example:
+
+```py
+import numpy as np
+import matplotlib.pyplot as plt
+grad = np.linspace(0,1,10000).reshape(100,100)
+plt.imshow(grad, cmap='gray')
+plt.show()
+
+plt.imshow(np.sin(np.linspace(0,10000,10000)).reshape(100,100) ** 2, cmap='gray')
+# does not work with QT_QPA_PLATFORM=wayland
+plt.show(block=False)
+plt.close()
+```
+
+ - Get the handwritten digits training data with `./download-mnist.sh`
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