Added Convolutional Autoencoders for data assimilation reference
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 * [MiniGrad](https://github.com/kennysong/minigrad) – A minimal, educational, Pythonic implementation of autograd (~100 loc).
 * [Map/Reduce implementations of common ML algorithms](https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
 * [BioPy](https://github.com/jaredthecoder/BioPy) - Biologically-Inspired and Machine Learning Algorithms in Python. **[Deprecated]**
+* [CAEs for Data Assimilation](https://github.com/julianmack/Data_Assimilation) - Convolutional autoencoders for 3D image/field compression applied to reduced order [Data Assimilation](https://en.wikipedia.org/wiki/Data_assimilation).
 * [SVM Explorer](https://github.com/plotly/dash-svm) - Interactive SVM Explorer, using Dash and scikit-learn
 * [pattern_classification](https://github.com/rasbt/pattern_classification)
 * [thinking stats 2](https://github.com/Wavelets/ThinkStats2)

Adds link to application of deep learning to Numerical modelling problem of [Data Assimilation](https://en.wikipedia.org/wiki/Data_assimilation). Recently published [paper](https://arxiv.org/abs/2101.02121) accompanies the code.  