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08_practicalmachinelearning

bgslibrary

A Background Subtraction Library

caffe-ocr

OCR with caffe deep learning framework

ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

clothes_parsing

Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

coursera-sna

Lecture Materials & Video (march 2013)

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

cs100.1xintrobigdatawithspark

Introduction to Big Data with Apache Spark on edx

dali

Source code for the Deformation and Lighting Invariant (DaLI) descriptor.

datasciencecoursera

datasharing

The Leek group guide to data sharing

developing-data-products

dl-docker

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.) with OpenCV Git and DIGITS.

dmml

code for ICCV19 paper "Deep Meta Metric Learning"

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

fast-reid

SOTA Re-identification Methods and Toolbox

gesture-tracking

A few alternate methods for gesture based interaction using Python

getting-and-cleaning-data-course-project

Getting and Cleaning Data Course Project

gfmm

Code for the paper 'Geodesic Finite Mixture Model'.

igftt

install_opencv

Shell scripts to install github version of OpenCV.

keras-framework

mdnet

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

melanoma-classification

:3rd_place_medal: (Bronze medal - 241st place - Top 8%) Repository for the "SIIM-ISIC Melanoma Classification" Kaggle competition.

opencv_docker

Docker images for OpenCV GIT, 3.1 and 3.0.

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

relevant_items

Find relevant tweets using Spark.

repdata_peerassessment1

Project Peer Assessment 1 from Reproducible Research

scikit-learn-book

Source code for the "Learning scikit-learn: Machine Learning in Python"