rakibhhridoy/ModelServingApplication-Tensorflow-Flask rakibhhridoy/ModelServingApplication-Tensorflow-Flask

  • Stars
    star
    2
  • Language
    Python
  • Created over 4 years ago
  • Updated over 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
rakibhhridoy/ModelServingApplication-Tensorflow-Flask
github

rakibhhridoy

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Tensorflow deep learning model serving using flask. The template is simple as main concern is building the web app. Template making quite easy than serving,it shows all the steps needed to linking the model with our web application.

More Repositories

1

AnomalyDetectionInTimeSeriesData-Keras

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.
Jupyter Notebook
15
star
2

rakibhhridoy.github.io

personal portfolio and blog site
HTML
2
star
3

WebAppClassifierStreamlit-Python

Machine Learning Training and Testing in Web App. It provide good learning interface for experimenting with different hyper parameter tuning and compare different algorithms with each other without writing code repeatedly.
Python
2
star
4

SentimentAnalysisInDashboard-WebApp

Sentiment analysis as dashboard in web server. Quick understandable and customized layout for any business application. This is based on positive, neutral and negative tweets in US location.
Python
2
star
5

CTScanPredictionCovid19-TensorFlow

Classifying covid positive and negative cases in ct-scan images. Though the data is not large enough, it can be processed and make prediction from the model. Images are quite similar thus the task became much complicated.
Jupyter Notebook
2
star
6

GoogleITCourse

This is a hands on specialization of Coursera Google IT Support Professional Certifications
1
star
7

DSBot

A experimental bot for automation.
Python
1
star
8

LinuxAdministration

Jupyter Notebook
1
star
9

ImageProcessing

Large amount of image processing is quite messy and time consuming,thus the working steps should be fast as well as accurate also. I've made sequential functions that is needed for processing data in TensorFlow and python. These functions made my work fast as it needed in commercial purposes.
Python
1
star
10

ClassifyOuterSignals-SpaceSignals

Space signals comes with huge noise in it. For analyzing the signals we have to make sure there is as less noise as possible. Detecting the noise and denoising the signals is quite hard to do. As a Data Science Analytics one should have the capability to handling any kind of dataset.
Jupyter Notebook
1
star
11

MachineLearning-FeatureSelection

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.
Python
1
star
12

ImageDenoisingUsing-AutoEncoders

Filtering out the noise presented in the image by auto-enconder algorithm in TensorFow and Keras. Rare images, unclean crime images,medical noise images can be denoised and find out the desired outcome by using auto-encoders.
Jupyter Notebook
1
star
13

EasyWayDiveInto-DataScience

Data Science is not as easy as it seems at first. The most problem faced by new learner are lack of resource knowledge as well as confusion in using the various resources. I hope this repository will benefit confusion learner.
Jupyter Notebook
1
star
14

ECommerce-BI

Business Intelligent in e-commerce, there are many part of it. This is project that based on e-commerce business analysis, model building, predictions and forecasting.
Jupyter Notebook
1
star
15

MachineLearningWebAppDeploy-Flask

Developing a web app of machine learning model using flask is quite easy. One should have some basic knowledge in web development,not so much but quite a bit. It is just a introductory web app in flask classifying cat vs dog by deep learning model.
HTML
1
star
16

BigDataAnalysisWithApacheSpark-StockPrice

Often we have to deal with large dataset, handling them with traditional method is quite tedious and time consuming. There's come the distributed method like apache spark. This repo consist distributed analysis of stock price which is quite large dataset.
Jupyter Notebook
1
star
17

ExploratoryDataAnalysis-Python

Exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
Jupyter Notebook
1
star
18

SupportVectorMachineIn-Medical

Support vector machine in medical disease detection. Both linear and non-linear data can be fitted in svm through its kernel specialization In medical we focus on precision or recall rather than accuracy.
Jupyter Notebook
1
star