python deep learning notebooks
I am using python to visualize data and learn from it.
- I added NMF feature reduction to analyze words in a document to build a better search engine using ensemblies
- I added Michelson vs Newcomb speed of light hypothesis testing code
- I feature analyze and extract using K-means cluster on the Iris classifier using K-means cluster
- I modeled the statistical distribution of the Junk bond market and determine it is not a normal distribution
- I modeled binomal and poisson distributions in python
- I modeled the trigometric function 5 times sin 1.5 times x plus pi divided 4
- I built a convolution neural net to learn an apple, banana, and orange image (deep learning - cnn - recognizing a list of images)
- I built a deep learning stocastic descent model to learn a trigonomy function (r=1-sin theta)
- I built a deep learning linear classifier to identify cultivator (deep learning intro with keras - linear classification.ipynb)
- I built a deep learning classifier to predict the flower types based on features (deep learning circle chasing.ipynb)
- I built a deep learning regressor to track the perimeters of a circle (deep learning circle chasing.ipynb)
- I applied gradient boost to predict death trends for three states (time series covid 19.ipynb)
- I applied linear regression and gradient boost to predict the morality rate trends (time series with machine learning.ipynb)
- I applied deep learning to predict mpg based in hp, weight, displacement, and gears (linear regressor mpg and horse power.ipynb)