Update README.md

cnn/README.md

@@ -6,9 +6,37 @@
 - Convolutional Neural Networks
 - Convolution, Activation(Relu), Pooling, Stride, Fully connected, Softmax
 
+### Quick, Draw! Data
+
+- [Data and Documentation](https://github.com/googlecreativelab/quickdraw-dataset)
+- [Video tutorial: Replaying Drawings with node server](https://thecodingtrain.com/challenges/122-quick-draw)
+- [Video tutorial: Replaying Drawings with Google Web API](https://thecodingtrain.com/challenges/122-quick-draw)
+- [Preparing Data as Images for Doodle Classifer Part 1](https://youtu.be/gX7U6WA7Ffk)
+- [Preparing Data as Images for Doodle Classifer Part 2](https://youtu.be/wMe6qcpD8jI)
+
+### Convolutional Neural Nets
+
+- [Original 1998 "LetNet5" paper: "Gradient-Based Learning Applied to Document Recognition"](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) by Y. Lecun, L. Bottou, Y. Bengio, P. Haffner
+- [Interactive Node-Link Visualizations of Convolutional Neural Networks](http://scs.ryerson.ca/~aharley/vis/)
+- [How computers got shockingly good at recognizing images](https://arstechnica.com/science/2018/12/how-computers-got-shockingly-good-at-recognizing-images/) by Timothy B. Lee
+- [Image Kernels Explained Visually](http://setosa.io/ev/image-kernels/) by Victor Powell
+- [A visual and intuitive understanding of deep learning, CNNs](https://www.youtube.com/watch?v=Oqm9vsf_hvU) (0:00 - 9:40) by Octavio Good
+
+### Examples
+
+- [Displaying MNIST](https://editor.p5js.org/ima_ml/sketches/ndqnn8p3F)
+- [Displaying Quick, Draw! 28x28 images](https://editor.p5js.org/ima_ml/sketches/wOO4nvwyw)
+- [p5.js Convolution demo](https://editor.p5js.org/codingtrain/sketches/BN1lE-gyl)
+- [p5.js Convolution demo -- max pooling](https://editor.p5js.org/codingtrain/sketches/GMRfsK7Wn)
+- [Training a model with `ml5.neuralNetwork()` and Google Quick, Draw! images](https://editor.p5js.org/ima_ml/sketches/bL6jONrFQ)
+- [Classifying Drawings with ml5's DoodleNet](https://editor.p5js.org/ima_ml/sketches/IbXlN6voN) (model trained by @yining1023)
+
 ## Coding session:
 
 - How to build a model in tf.js: Doodle Classifier!
+- [p5.js Convolution demo](https://editor.p5js.org/codingtrain/sketches/BN1lE-gyl)
+- [p5.js Convolution demo -- max pooling](https://editor.p5js.org/codingtrain/sketches/GMRfsK7Wn)
+- [Training a model with `ml5.neuralNetwork()` and Google Quick, Draw! images](https://editor.p5js.org/ima_ml/sketches/bL6jONrFQ)
 
 ## See demos live
 
@@ -26,9 +54,16 @@
 - [NeuralNetwork_ImageClassifier_Letters](https://yining1023.github.io/machine-learning-for-the-web/cnn/NeuralNetwork_ImageClassifier_Letters)
 - [NeuralNetwork_ImageClassifier_Video](https://yining1023.github.io/machine-learning-for-the-web/cnn/NeuralNetwork_ImageClassifier_Video)
 
-## [Homework](https://github.com/yining1023/machine-learning-for-the-web/wiki/Week-11-2020-Fall)
+## [Assignment](https://github.com/yining1023/machine-learning-for-the-web/wiki/Week-11-2020-Fall)
+
+### Reading
+- [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) by Ujjwal Karn
+- [Exploring and Visualizing an Open Global Dataset](https://research.googleblog.com/2017/08/exploring-and-visualizing-open-global.html) by Google Research
+### Coding
+- Use either of the examples to DoodleNet ([examples](https://editor.p5js.org/yining/sketches/LYg2Dw0mB)) 
+- Or Train your CNN ([examples](https://editor.p5js.org/ima_ml/sketches/bL6jONrFQ)) 
 
-### Video Tutorials
+## Video Tutorials
 
 - [What is Convolutional Neural Network Part 1](https://youtu.be/qPKsVAI_W6M?list=PLRqwX-V7Uu6YPSwT06y_AEYTqIwbeam3y)
 - [What is Convolutional Neural Network Part 2](https://youtu.be/pRWq_mtuppU?list=PLRqwX-V7Uu6YPSwT06y_AEYTqIwbeam3y)