USmm2018's repositories
covid-19
A collection of work related to COVID-19
bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
DU_BigData_hw5_Matplotlib-Pyber
* You must use the Pandas Library and the Jupyter Notebook. * You must use the Matplotlib library. * You must include a written description of three observable trends based on the data. * You must use proper labeling of your plots, including aspects like: Plot Titles, Axes Labels, Legend Labels, X and Y Axis Limits, etc. * Your scatter plots must include [error bars](https://en.wikipedia.org/wiki/Error_bar). This will allow the company to account for variability between mice. You may want to look into [`pandas.DataFrame.sem`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sem.html) for ideas on how to calculate this. * Remember when making your plots to consider aesthetics! * Your legends should not be overlaid on top of any data. * Your bar graph should indicate tumor growth as red and tumor reduction as green. It should also include a label with the percentage change for each bar. You may want to consult this [tutorial](http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/) for relevant code snippets. * See [Starter Workbook](Pymaceuticals/pymaceuticals_starter.ipynb) for a reference on expected format. (Note: For this example, you are not required to match the tables or data frames included. Your only goal is to build the scatter plots and bar graphs. Consider the tables to be potential clues, but feel free to approach this problem, however, you like.) ## Hints and Considerations * Be warned: These are very challenging tasks. Be patient with yourself as you trudge through these problems. They will take time and there is no shame in fumbling along the way. Data visualization is equal parts exploration, equal parts resolution.
sPyNNaker8
The PyNN 0.8 interface to sPyNNaker.
netpyne
A python package to facilitate the development of biological neuronal networks in NEURON
dopamine
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
nrn
NEURON Simulator. (iv required for the GUI)
pandas-ml
pandas, scikit-learn, xgboost and seaborn integration
pandas_exercises
Practice your pandas skills!
keras
Deep Learning for humans
nengo-detailed-neurons
Enable Nengo to use neuron models simulated in NEURON.
keras-vis
Neural network visualization toolkit for keras
Predictive-Modelling-using-Stepwise-Eliminaton-Method
Predictive modelling of miles per gallon and critically evaluating the step-wise elimination method.
nengo
A Python library for creating and simulating large-scale brain models
brian2
Brian is a free, open source simulator for spiking neural networks.
tensorflow-mnist-tutorial
Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.
quiver
Interactive convnet features visualization for Keras
snn_toolbox
Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.
python-machine-learning-book-2nd-edition
The "Python Machine Learning (2nd edition)" book code repository and info resource
Blog
A collection of resources and Jupyter notebooks from my blog.
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
lm-snn
Using spiking neurons and spike-timing-dependent plasticity to classify the MNIST handwritten digits.
nasa-latex-docs
Official Documentation:
thesemicolon
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
muffin-cupcake
classifying muffin and cupcake recipes using support vector machines
Spiking-Neural-Network
Pure python implementation of SNN
Python
Day-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. It also includes cheat-sheets, references which are logged daily to accelerate your learning.
pyspace
Signal Processing And Classification Environment in Python
SimulationTutorials
Public tutorials around electrophysiological simulations