Algorithm Notebooks
Various Jupyter notebooks for learning new algorithms. A majority of the ML
implementations use MNIST.
Any applicable credits are within the notebooks, scripts, and modules. Includes:
- Generative Adversarial Networks
- Deep Convolutional
- Conditional Wasserstein with Gradient Penalty
- Inversion
- Gaussian Process Pregression
- Carbon dioxide data set
- General tutorial with derivations
- xsinx using scikit learn
- xsinx using MCMC
- JWST
- Making source cutouts from a Webb public release observation
- Analyze source cutouts using PCA, KMeans, and KDE
- Miscellaneous
- Self organizing maps
- Symbolic regression with Newtonian gravity
- Stochastic gradient descent on linear regression
- Principal component analysis on double gaussians
- Kernel density estimation
- Tensorflow
- ResNet classification
- Time series regression
- Time Series Analysis
- Dimensionality Reduction
- Functional principal component analysis on temperature data
- Functional principal component analysis on height data
- Principal component analysis on signals with oscillating amplitudes
- Forecasting
- ARIMA on airline passengers dataset
- Exponential Smoothing on airline passengers dataset
- Comparing ARIMA and exponential smoothing
- Signal Processing
- Fast Fourier Transform
- Lomb Scargle Periodograms
- Phase folding
- UNET
Installations
There are three environments: one purely conda (environment_conda.yml
; only
pip libraries are tensorflow-macos
, tensorflow-metal
and minisom
since
they do not have conda support), one purely pip (requirements.txt
; only conda
library needed is scikit-fda
due to incompatibility in pip), and one mix
(environment.yml
; preferred). All notebooks were tested in the preferred
environment using a 2021 Mac M1 chip.
# create preferred env
conda env create -f environment.yml
# create conda env
conda env create -f environment_conda.yml
# create pip env
conda create -n home_pip python=3.11 pip
conda activate home_pip
pip install -r requirements.txt
conda install -c conda-forge scikit-fda
Comparing models
- Plot data (train and test) vs model (train and test)
- Residuals plot and histogram
- Plot y_test vs y_pred
- MSE, MAE, and Pearson correlation