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2014-summer-travels

Python-based spatial data analysis and visualization of the GPS location data from my 2014 summer travels.

android-udev-rules

Android udev rules list aimed to be the most comprehensive on the net

android_porting

step by step porting on android device

atom_default_keymap

Atom shortcuts,快捷键翻译,基于最新1.8

awesome-model-compression-and-acceleration

caffe

Caffe: a fast open framework for deep learning.

caffe-alexnet-overfeat

Generate random image buffers, instantiate hybrid alexnet-overfeat architecture, then begin training

cityscapesscripts

README and scripts for the Cityscapes Dataset

convnet-benchmarks

Easy benchmarking of all publicly accessible implementations of convnets

cs231n.github.io

Public facing notes page

deep-learning-benchmark

Deep Learning Benchmark for comparing the performance of DL frameworks, GPUs, and single vs half precision

digits

Deep Learning GPU Training System

django-celery

Example of how to handle background processes with Django, Celery, and Docker

gpxsee

GPS log file viewer and analyzer with support for GPX, TCX, KML, FIT, IGC, NMEA and OziExplorer files.

hunspell_dicts

Hunspell dictionaries for PostgreSQL

kitti_frc_detection

Faster RCNN for KITTI

kmeans_clustering_in_3dimensions

Implementation of Kmeans clustering in three dimensions using sklearn and matplotlib

kmeans_vr

latlon23

Methods for representing geographic coordinates

machine-learning-samples

machinelearning

Basic Machine Learning and Deep Learning

mobilenet

MobileNet build with Tensorflow

mobilenet-ssd

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

mobilenetv2

mobilenetv2-1

A Tensorflow implementation of MobileNet V2

models

Models and examples built with TensorFlow

nds-tiles

A simple java implementation of the NDS Tiling Scheme

object_detection_kitti

TF Object Detection on Kitti Data

path-prediction

The problem that we have chosen to solve is navigation path planning from a source to a set of destinations. This problem in other words is a Travelling Salesperson problem. Now, solving this problem has many advantages in many areas like planning, logistics, and manufacture of microchips. It is a sub-problem in many areas such as DNA sequencing. Few problems that can be solved.

pbpython

Code, Notebooks and Examples from Practical Business Python