alysons Goto Github PK
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Active attention in image classification
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art
A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error = 0.34%.
A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules
With reconstruct, capsule representation, adversarial experiments. Implementation of NIPS2017 paper "Dynamic Routing Between Capsules" in tensorflow.
A Tensorflow implementation of CapsNet(Capsules Net) apply on german traffic sign dataset
Another implementation of Hinton's capsule networks in tensorflow.
Convolutional Neural Networks with Alternately Updated Clique (to appear in CVPR 2018)
Fine-tune CNN in Keras
剑指Offer
吴恩达老师的机器学习课程个人笔记
some materials about deep learning on medical image like x-rays, MRI, CT
Convolutional Neural Network using Tensor Flow to perform brain segmentation
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
DenseNet implementation in Keras
Detection of Diabetic Retinopathy using CNN , KNN ,SVM
DualNet: Learn Complementary Features for Image Recognition
Face recognition using Tensorflow
谷歌INCEPTION-RESNET-V2迁移学习实现图像二分类判断图像是否生病
keras densenet40层网络结构,实现眼底图二分类。
谷歌INCEPTION-RESNET-V3迁移学习实现图像二分类判断图像是否生病
this is a test
Deep Learning for humans
A Keras implementation of YOLOv3 (Tensorflow backend)
knowledge distillation for classification
Machine learning algorithms implemented by pure numpy
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.
Models and examples built with TensorFlow
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.