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lung-segmentation

Lung segmentation for chest X-Ray images

machine-learning

A collection of Machine Learning algorithms in Python written from scratch.

machine-learning-project-for-image-compression

Implementation & Learning of Compression of Image through use of K-means Clustering Algorithm

machine_learning

machinelearningwebappdeploy-flask

Developing a web app of machine learning model using flask is quite easy. One should have some basic knowledge in web development,not so much but quite a bit. It is just a introductory web app in flask classifying cat vs dog by deep learning model.

magenta

Magenta: Music and Art Generation with Machine Intelligence

make_a_neural_network

This is the code for the "Make a Neural Network" - Intro to Deep Learning #2 by Siraj Raval on Youtube

mark-incognito

A chrome extension to help users with managing domains to incognito mode only

markdown-here

Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

mask-rcnn

An implementation of mask-rcnn for self-driving cars

mask-rcnn-shiny

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

material

Material design for AngularJS

material-design-icons

Material Design icons by Google

material-design-lite

Material Design Components in HTML/CSS/JS

materialdesigninxamltoolkit

Google's Material Design in XAML & WPF, for C# & VB.Net.

mathematics-for-machine-learning-coursera

quizzes/assignments for mathematics for machine learning specialization on coursera

mathematics-of-deep-learning

The Mathematics of Deep Learning, SIPB IAP 2018

mcm

MCM file compressor

mdmm

Learning multi-domain multi-modality I2I translation

mdn-learning-area

Github repo for the MDN Learning Area.

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

medical-data

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.

medical-image-reconstruction-with-convolutional-autoencoders

MRI Image reconstruction using a Convolutional AutoEncoder

medical-imaging

Medical Image Processing Techniques

medical-imaging-datasets

A list of Medical imaging datasets.

medium-zoom

🔎🖼 A JavaScript library for zooming images like Medium

medpy

Medical image processing in Python

mef-gan

This is the code for multi-exposure image fusion via generative adversarial networks