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barchart3d-plotly

3D bar chart for Plotly (python)

car-recognition

Car Recognition with Deep Learning

coursera-applied-machine-learning-with-python

This repository includes course assignments of applied machine learning with python on coursera by university of michigan.

darknet

Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)

deep-learning

deep_sort_yolov3

Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow

experimenting-with-sort

Experimenting with sort different classical tracking algorithms for real time multiple object tracking (MOT)

houses-dataset

This is the first benchmark dataset for houses prices that contains both images and textual information that was introduced in our paper.

iem

Code that makes the Iowa Environmental Mesonet run, or run into the ground.

imv_lstm

Pytorch implementation of "Exploring Interpretable LSTM Neural Networks over Multi-Variable Data" https://arxiv.org/pdf/1905.12034.pdf

iou-tracker

Python implementation of the IOU Tracker

labelimg

:metal: LabelImg is a graphical image annotation tool and label object bounding boxes in images

lstm_rnn_tutorials_with_demo

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

node-moving-things-tracker

Realtime tracker by detections wrote in javascript for node.js / browsers

openio-canada

Module to create symmetric Environmentally Extended Input-Output tables for Canada.

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

parkmaster

ParkMaster: An in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments

rides-python-sdk

Uber Rides Python SDK (beta)

ssd_sort

Multi-person tracking with SSD and Sort

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

timeseries2dbarchartimagecnn

Conversion of the time series values to 2-D stock bar chart images and prediction using CNN (using Keras-Tensorflow)

tracking-with-darkflow

Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow

vehicledetectionandtracking

Modified TensorFlow Object Detection Model for vehicle detection and tracking

vrp-rl

Reinforcement Learning for Solving the Vehicle Routing Problem

wxapp_secondhandmall

二手交易平台（wxapp）

yolo3-4-py

A Python wrapper on Darknet. Compatible with YOLO V3.