方立超's starred repositories
spring-cloud-stream
Framework for building Event-Driven Microservices
hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference
Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required
kafka-streams-machine-learning-examples
This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.
tc-nameko-practice
『Microservices & Nameko』Python 微服务实践
QYQXDeepLearning
DeepLearning
data_analysis
基于Python的南京二手房数据采集及可视化分析
C-Plus-Plus
Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
predicting-cloud-CPU-utilization-on-Azure-dataset-using-deeplearning
Many companies are utilizing the cloud for their day to day activities. Many big cloud service providers like AWS, Microsoft Azure have been success-fully serving its increasing customer base. A brief understanding of the char-acteristics of production virtual machine (VM) workloads of large cloud pro-viders can inform the providers resource management systems, e.g. VM scheduler, power manager, server health manager. In our project we will be analysing Microsoft Azure’s VM CPU utilization dataset released in October 2017. We predict the VM workload from the CPU usage pattern like mini-mum, maximum and average from the Azure dataset. Different techniques among Deep learning are used for the prediction by considering the history of the workload. By considering real VM traces, we can show that the predic-tion-informed schedules increase utilization and stop physical resource ex-haustion. We can arrive at a conclusion that cloud service providers can use their workloads’ characteristics and machine learning techniques to enhance resource management greatly.
flink-training
Apache Flink Training Excercises
God-Of-BigData
专注大数据学习面试，大数据成神之路开启。Flink/Spark/Hadoop/Hbase/Hive...
dl-on-flink
Deep Learning on Flink aims to integrate Flink and deep learning frameworks (e.g. TensorFlow, PyTorch, etc) to enable distributed deep learning training and inference on a Flink cluster.
SamplingAug
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution (BMVC2021)
super-resolution
Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution
fast-sr-unet
Implementation of the paper "Fast video visual quality and resolution improvement using SR-UNet".
dcscn-super-resolution
A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model.
Super-Resolution.Benckmark
Benchmark and resources for single super-resolution algorithms
tensorflow-vdsr
A tensorflow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", CVPR 16'
Yolo-FastestV2
:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+