python-deep-learning

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)

Name		Name	Last commit message	Last commit date
Latest commit History 41 Commits
5 times sin 1.5 times x plus pi divided 4.ipynb		5 times sin 1.5 times x plus pi divided 4.ipynb
CNN image classification.ipynb		CNN image classification.ipynb
Deep learning with Keras using resnet50.ipynb		Deep learning with Keras using resnet50.ipynb
Michaelson speed of light - hypothesis testing.ipynb		Michaelson speed of light - hypothesis testing.ipynb
NMF feature reduction.ipynb		NMF feature reduction.ipynb
Principal component Analysis (PCA) on Wine dataset.ipynb		Principal component Analysis (PCA) on Wine dataset.ipynb
README.md		README.md
Realtime R0.ipynb		Realtime R0.ipynb
apple.jpg		apple.jpg
apples.jpg		apples.jpg
banana.jpg		banana.jpg
banknote.csv		banknote.csv
cars_mpg_tree_classifiers.csv		cars_mpg_tree_classifiers.csv
clustering in python.txt		clustering in python.txt
clustering.ipynb		clustering.ipynb
cohort analysis.txt		cohort analysis.txt
cross_val_score.txt		cross_val_score.txt
decision tree machine learning.txt		decision tree machine learning.txt
deep learning - cnn - recognizing a list of images.ipynb		deep learning - cnn - recognizing a list of images.ipynb
deep learning addition.ipynb		deep learning addition.ipynb
deep learning circle chasing.ipynb		deep learning circle chasing.ipynb
deep learning intro with keras - linear classification.ipynb		deep learning intro with keras - linear classification.ipynb
deep learning iris.ipynb		deep learning iris.ipynb
deep learning tensors and layers and encoders.txt		deep learning tensors and layers and encoders.txt
deep learning using convolution neural networks part 2.txt		deep learning using convolution neural networks part 2.txt
deep learning using convolution neural networks.txt		deep learning using convolution neural networks.txt
deep learning with keras - binary classification.ipynb		deep learning with keras - binary classification.ipynb
deep learning with keras - binary classification.txt		deep learning with keras - binary classification.txt
deep learning with keras - images.txt		deep learning with keras - images.txt
deep learning with keras - linear classification introduction.txt		deep learning with keras - linear classification introduction.txt
deep learning with keras - ltsm.txt		deep learning with keras - ltsm.txt
deep learning with keras hyperparameter.txt		deep learning with keras hyperparameter.txt
deep learning with keras.txt		deep learning with keras.txt
deep learning.txt		deep learning.txt
iris-classifier k-means clustering.ipynb		iris-classifier k-means clustering.ipynb
k means clustering.txt		k means clustering.txt
linear regression.txt		linear regression.txt
linear regressor mpg and horse power.ipynb		linear regressor mpg and horse power.ipynb
logistic and linear regression.txt		logistic and linear regression.txt
logistic regression.txt		logistic regression.txt
machine learning for business.txt		machine learning for business.txt
machine learning for finance.txt		machine learning for finance.txt
machinelearning links.txt		machinelearning links.txt
matplotlib.txt		matplotlib.txt
michaelson_speed_of_light.csv		michaelson_speed_of_light.csv
neural network.ipynb		neural network.ipynb
numpy statistics.txt		numpy statistics.txt
orange.jpg		orange.jpg
pandas data manipulation.txt		pandas data manipulation.txt
pandas time series.txt		pandas time series.txt
predictions.csv		predictions.csv
python commands.txt		python commands.txt
python for finance.txt		python for finance.txt
python-deep-learning-master.zip		python-deep-learning-master.zip
r=1-sin theta.ipynb		r=1-sin theta.ipynb
rfm segmentation.txt		rfm segmentation.txt
ridge.txt		ridge.txt
seaborn.txt		seaborn.txt
statistical thinking part 1.txt		statistical thinking part 1.txt
statistical thinking part 2.txt		statistical thinking part 2.txt
statistics part 1.ipynb		statistics part 1.ipynb
statistics poisson junk bond market.ipynb		statistics poisson junk bond market.ipynb
svm (support vector machine).txt		svm (support vector machine).txt
time series covid 19.ipynb		time series covid 19.ipynb
time series with machine learning.ipynb		time series with machine learning.ipynb
wine.data.csv		wine.data.csv

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