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activeattention

Active attention in image classification

attention-gated-networks

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

capsnet

CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art

capsnet-keras

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.

capsnet-tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

capsnet-tensorflow-1

With reconstruct, capsule representation, adversarial experiments. Implementation of NIPS2017 paper "Dynamic Routing Between Capsules" in tensorflow.

capsnet-traffic-sign-classifier

A Tensorflow implementation of CapsNet(Capsules Net) apply on german traffic sign dataset

capsules-tensorflow

Another implementation of Hinton's capsule networks in tensorflow.

cliquenet

Convolutional Neural Networks with Alternately Updated Clique (to appear in CVPR 2018)

cnn_finetune

Fine-tune CNN in Keras

coding-interviews

剑指Offer

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

deep-learning-on-medical-image

some materials about deep learning on medical image like x-rays, MRI, CT

deeplearning-for-mri

Convolutional Neural Network using Tensor Flow to perform brain segmentation

densenet

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

densenet-1

DenseNet implementation in Keras

diabetic-retinopathy-project-deep-learning-

Detection of Diabetic Retinopathy using CNN , KNN ,SVM

dualnet

DualNet: Learn Complementary Features for Image Recognition

facenet

Face recognition using Tensorflow

fundus_binary_classification

谷歌INCEPTION-RESNET-V2迁移学习实现图像二分类判断图像是否生病

fundus_densenet40_binary_classification

keras densenet40层网络结构，实现眼底图二分类。

fundus_transfer_v3_classification

谷歌INCEPTION-RESNET-V3迁移学习实现图像二分类判断图像是否生病

hello-world

this is a test

keras

Deep Learning for humans

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

keras_demo

knowledge_distillation

knowledge distillation for classification

machinelearning

Machine learning algorithms implemented by pure numpy

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.

models

Models and examples built with TensorFlow