jimmy-ksu Goto Github PK

10-steps-to-become-a-data-scientist

15-minute-apps

15 minute (small) desktop apps built with PyQt

2018-05-22-practiceworkshop

336k

Lecture Notes for Upper Division Undergraduate Newtonian Dynamics Course

active_subspaces

Python Active-subspaces Utility Library

aeropython

Classical Aerodynamics of potential flow using Python, for Prof. Barba's course at GW (1st version:Spring 2014, 2nd run:2015).

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

ann-visualizer

A python library for visualizing Artificial Neural Networks (ANN)

applied-predictive-modeling-with-python

A collection of notebook to learn the Applied Predictive Modeling using Python.

applied_reactor_physics

Applied Reactor Physics

arbiter

A concurrent task-runner that automatically resolves dependency issues

arima

Simple python example on how to use ARIMA models to analyze and predict time series.

article-tutorials

Articles related to Neural Networks will be posted here.

as-data-sets

Active Subspace Data Sets

auto-sklearn

Automated Machine Learning with scikit-learn

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-mecheng

Awesome Mechanical Engineering Resources

awesome-python-decorator

A curated list of awesome python decorator resources.

basic_ml

A work in progress towards stacking all the data science, data management and machine learning training materials [in russian].

berkeley-stat-157-deep-learning

Assignments from Berkeley's stat157 Deep Learning course. This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.

big_data_class

black

The uncompromising Python code formatter

blind_deconvolution

Blind deconvolution using the idea from "Blind Deconvolution using Convex Programming", from Ali Ahmed, Benjamin Recht, and Justin Romberg

bokeh-notebooks

Interactive Web Plotting with Bokeh in IPython notebook

buckling-ml

Predict concrete column dimensions with machine learning.

carnd-unscented-kalman-filter-project-master

2nd Project for Self Driving Car ND Term 2 - UKF

cashflows

Financial investment modeling and advanced engineering economics using Python

cgen

C/C++ source generation from an AST

chaospy

Chaospy - Toolbox for performing uncertainty quantification.

chtmultiregionsimplefoam-example

chtMultiRegionSimpleFoam example with applied flux and solid sub cycles