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anomalydetection

Twitter's Anomaly Detection in Pure Python

azure-iot-predictive-maintenance

Azure IoT Predictive Maintenance preconfigured solution

bayesian-optimization-in-high-dimensional-input-space

This is the repository for implementation of different methods in high-dimensional BO.

ceal-medical-image-segmentation

Active Deep Learning for Medical Imaging Segmentation

computer-vision-project

Use deep learning to segment and classify cancerous cells in medical images

deep-forecast

The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

deepdive

DeepDive

dl-to-retina-images

Deep learning applied to human retina images for medical diagnosis support.

fcis

Fully Convolutional Instance-aware Semantic Segmentation

gcforest

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

labelimg

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

medical-image-analysis

Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning and Deformable models

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-segmentation

Segmentation of Abdomen Organs from Magnetic Resonance Image using Deep Learning Techniques

medicalimagesegmentation

This repository aims at containing all the code employed at LIVIA to segment medical images. Mainly, our research focuses on bringind the expertise in deep learning and optimization techniques to the medical image analysis domain.

mydeepcoder

Implememt of MS's deepcoder

one-hundred-layers-tiramisu

Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

opencv-python-tutorial

OpenCV-Python-Toturial-中文版.pdf 源代码

predictive-maintenance-using-lstm

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

prognosticsalgorithmlibrary

prognosticsmodellibrary

reinforcement-learning-with-tensorflow

Reinforcement learning tutorials

robot-grasp-detection

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.

segnet-tutorial

Files for a tutorial to train SegNet for road scenes using the CamVid dataset

star_wars_object_detection

Using Tensorflow's Object Detection API to detect R2-D2 and BB-8 from Star Wars.

stick-breaking_dgms

Deep Generative Models with Stick-Breaking Priors

tensorflow-convolutional-autoencoder

This is an implementation of Convolutional AutoEncoder using only TensorFlow

tensorflow-examples

TensorFlow Tutorial and Examples for beginners

tensorflow-mnist-vae

Tensorflow implementation of variational auto-encoder for MNIST