sslpp Goto Github PK

23_getting_shapefile_from_osm

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

applied-predictive-modeling-with-python

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

appliedpredictivemodeling

Data and code from Applied Predictive Modeling (2013)

apricot

apricot implements submodular selection for the purpose of selecting subsets of massive data sets to train machine learning models quickly.

arcgis-python-api

Documentation and samples for ArcGIS API for Python

bayesian-belief-networks

Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.

bookrepo

clevr-graph

CLEVR graph: A dataset for graph based reasoning

code_snippets

comp_576_deeplearning_class

Comp 576

cs228_pgm

🌀 Stanford CS 228 - Probabilistic Graphical Models

dbda-python

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

dbn_cnn_lfd

Interface between a DBN model and CNN models to learn from demonstrations

densenet-tensorflow

Simple Tensorflow implementation of Densenet using Cifar10, MNIST

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

difftaichi

10 differentiable physical simulators built with Taichi differentiable programming (DiffTaichi, ICLR 2020)

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

fastbook

The fastai book, published as Jupyter Notebooks

geojsontoshpfile

This code converts GeoJSON to shape files.

google-images-download

Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!

graph_nets

Build Graph Nets in Tensorflow

hands-on-reinforcement-learning-with-python

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

jekyll

Jekyll-based static site for The Programming Historian

jhepc

John Hunter Excellence in Plotting Contest

jhepc-1

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

keras-applications

Reference implementations of popular deep learning models.

lime

Lime: Explaining the predictions of any machine learning classifier