Tulin2010's Stars
google-research/google-research
Google Research
CCIIPLab/EACM
The source code of the paper "Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction"
umbertogriffo/Predictive-Maintenance-using-LSTM
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
thangbk2209/LSTM_GoogleClusterTraceData
xibinyue/ConvLSTM-1
Convolutional LSTM for Precipitation Nowcasting
thu-coai/ecm
This project is a tensorflow implement of our work, ECM (emotional chatting machine).
aymericdamien/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
UniqueAndys/Host-Load-Prediction-with-LSTM
host load prediction with Long Short-Term Memory in cloud computing
pabaldonedo/autoencoder
Sparse autoencoder network following Andrew Ng lecture notes in CS294A.
GhadaSokar/Sparse-Denosing-AutoEncoder
A pure implementation for sparse denoising autoencoder with adaptive evolutionary training using Scipy. The sparse implementation makes the algorithm scalable to high dimensional data and trainable on CPUs.
benedekrozemberczki/DANMF
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
sheydashz/federated-double-deep-Q_network-
A framework that exploits the potentials of distributed federated learning and double deep Q-networks to minimize joint energy and delay in IoT networks
XiaTiancong/Deep-Reinforcement-Learning-for-IoT-Network-Dynamic-Clustering-in-Edge-Computing
mehdimo/spatio-temporal-ensemble-deep-learning
Ensemble of deep learning models using Spatio-temporal patterns in data
AAnirudh07/Aposemat-IoT23-Network-Classification
Binary classification of network data using machine learning algorithms
wenhwu/awesome-remote-sensing-change-detection
List of datasets, codes, and contests related to remote sensing change detection
BiswajitPadhi99/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.
backbencher00/stockprice_prediction_LSTM_GRU
stock price prediction by hybride model of LSTM and GRU
KedarP5/Cloud_Resources_Prediction_LSTM
Cloud resource prediction with the help of LSTM(Long Short-Term Memory)
amcs1729/Predicting-cloud-CPU-usage-on-Azure-data
Forecasting future CPU Usage in Azure VM using Deep Learning Models. Compares LSTM , GRU and IndRNN
Azure/AzurePublicDataset
Microsoft Azure Traces
tunz/transformer-pytorch
Transformer implementation in PyTorch.
chasebk/code_FLNN
Prediction google trace data using Functional Link Neural Network and Optimization Algorithms such as GA, PSO, ABC,...
Vu5e/JobFailurePredictionGoogleTraces2019
By learning and using prediction for failures, it is one of the important steps to improve the reliability of the cloud computing system. Furthermore, gave the ability to avoid incidents of failure and costs overhead of the system. It created a wonderful opportunity with the breakthroughs of machine learning and cloud storage that utilize generated huge data that provide pathways to predict when the system or hardware malfunction or fails. It can be used to improve the reliability of the system with the help of insights of using statistical analysis on the workload data from the cloud providers. This research will discuss regarding job usage data of tasks on the large “Google Cluster Workload Traces 2019” dataset, using multiple resampling techniques such as “Random Under Sampling, Random Oversampling and Synthetic Minority Oversampling Technique” to handle the imbalanced dataset. Furthermore, using multiple machine learning algorithm which is for traditional machine learning algorithm are “Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier and Extreme Gradient Boosting Classifier” while deep learning algorithm using “Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)” for job failure prediction between imbalanced and balanced dataset. Then, to have a comparison of imbalanced and balanced in terms of model accuracy, error rate, sensitivity, f – measure, and precision. The results are Extreme Gradient Boosting Classifier and Gradient Boosting Classifier is the most performing algorithm with and without imbalanced handling techniques. It showcases that SMOTE is the best method to choose from for handling imbalanced data. The deep learning model of LSTM and Gated Recurrent Unit may be not the best for the in terms of accuracy, based on the ROC Curve its better than the XGBoost Classifier and Gradient Boosting Classifier.
JavadDogani/Multivariate-Cloud-workload-analysis
This repository analyzes the Multivariate workload data of Google Cluster machines.
NGYB/Stocks
Programs for stock prediction and evaluation
huseinzol05/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
joinalahmed/upgrad_capstore_iitm_aws_iotgreengrass_lstm
A time-series forecasting model to predict air pollution levels for each day which enables maintenance of the air filtration system. This capstone project showcases how AWS IoT and Amazon SageMaker services can deliver an end-to-end IoT+ML project.
liaq192/LSTM-Networking
LSTM applied on a CPU Usage dataset.
Wei2Wakeup/Google_Cluster_Trace