flasgger

lasgger is a Flask extension to extract OpenAPI-Specification from all Flask views registered in your API. Flasgger also comes with SwaggerUI embedded so you can access http://localhost:5000/apidocs and visualize and interact with your API resources. Flasgger also provides validation of the incoming data, using the same specification it can validates if the data received as as a POST, PUT, PATCH is valid against the schema defined using YAML, Python dictionaries or Marshmallow Schemas. Flasgger can work with simple function views or MethodViews using docstring as specification, or using @swag_from decorator to get specification from YAML or dict and also provides SwaggerView which can use Marshmallow Schemas as specification. Flasgger is compatible with Flask-RESTful so you can use Resources and swag specifications together, take a look at restful example. Flasgger also supports Marshmallow APISpec as base template for specification, if you are using APISPec from Marshmallow take a look at apispec example.

flye

gas-sensor-array-under-dynamic-gas-mixtures

Time series data for Ethylene and Methane in air, and Ethylene and CO in air

gas-sensors-for-home-activity-monitoring-data-set

Abstract: 100 recordings of a sensor array under different conditions in a home setting: background, wine and banana presentations. The array includes 8 MOX gas sensors, and humidity and temperature sensors. Source: Creators: Flavia Huerta,Gaurav Gawade Ramon Huerta, University of California San Diego, USA Donors: Flavia Huerta Ramon Huerta, University of California San Diego, USA (rhuerta ‘@’ ucsd.edu) Thiago Mosqueiro, University of California San Diego, USA (thmosqueiro ‘@’ ucsd.edu) Jordi Fonollosa, Institute for Bioengineering of Catalunya, Spain (jfonollosa ‘@’ ibecbarcelona.eu) Nikolai Rulkov, University of California San Diego, USA ( nrulkov ‘@’ ucsd.edu ) Irene Rodriguez-Lujan, Universidad Autonoma de Madrid, Spain ( Irene.rodriguez ‘@’ uam.es ) Data Set Information: This dataset has recordings of a gas sensor array composed of 8 MOX gas sensors, and a temperature and humidity sensor. This sensor array was exposed to background home activity while subject to two different stimuli: wine and banana. The responses to banana and wine stimuli were recorded by placing the stimulus close to the sensors. The duration of each stimulation varied from 7min to 2h, with an average duration of 42min. This dataset contains a set of time series from three different conditions: wine, banana and background activity. There are 36 inductions with wine, 33 with banana and 31 recordings of background activity. One possible application is to discriminate among background, wine and banana. This dataset is composed of two files: HTsensordataset.dat (zipped), where the actual time series are stored, and the HTSensormetadata.dat, where metadata for each induction is stored. Each induction is uniquely identified by an id in both files. Thus, metadata for a particular induction can be easily found by matching columns id from each file. We also made available python scripts to exemplify how to import, organize and plot our data. The scripts are available on GitHub: https://github.com/gauravgawade951999/gauravgit For each induction, we include one hour of background activity prior to and after the stimulus presentation. Time series were recorded at one sample per second, with minor variations at some data points due to issues in the wireless communication. For details on which sensors were used and how the time series is organized, see Attribute Information below.

generative-adversarial-network

a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. Compatable with python 3.6 Data set net cifar 10

github-

heroku-

this code is for self learning purpose. in this code we have deploy the a simple web app code with static file using flask on heroku server

heroku-demo

https-github.com-nitinguptadu-to-acess-gpu-from-local-computer-in-pytorch-and-keras-

image-classifiaction-using-resnet-

This Model Runs on Free Heroku server .Static page is inside app.py . Model is loaded by keras application

image-classification

Classification of MNIST data set

image-classification-heroku-

This code is for self learning purpose

image-classification-model-deployment-using-flask-

image classifiacion model with flask using keras

image-to-text-using-object-detection-

This repo is for self learning purpose

implementing-autoencoders-in-keras-tutorial

In this tutorial, you’ll learn about autoencoders in deep learning and you will implement a convolutional and denoising autoencoder in Python with Keras. You will work with the NotMNIST alphabet dataset as an example

isbi-challenge-segmentation-of-neuronal-structures-in-em-stacks

In this challenge, a full stack of EM slices will be used to train machine learning algorithms for the purpose of automatic segmentation of neural structures. The images are representative of actual images in the real-world, containing some noise and small image alignment errors. None of these problems led to any difficulties in the manual labeling of each element in the image stack by an expert human neuroanatomist. The aim of the challenge is to compare and rank the different competing methods based on their pixel and object classification accuracy.

keras---python-deep-learning-neural-network-api

This series will teach you how to use Keras, a neural network API written in Python. Each video focuses on a specific concept and shows how the full implementation is done in code using Keras and Python. We will learn how to preprocess data, organize data for training, validation and testing, build an artificial neural network from scratch, train an artificial neural network, build a convolutional neural network (CNN) and much more!

keras-cnn-for-regressiion-and-classification--with-multi-target-

This Repo is for self learning purpose

keras-gpu-installization-for-yolo-v3-for-text-detection-

Gpu GTX 1650 with 4 gb Graphic Ram Cuda version 10 tensorflow-gpu==1.13.1

keras-ocr-

this repo is for self learning purpose

key-word-recognition-using-cnn

key word recognition ( one to nine ) using CNN

linear-regresssion

lungs-segmentation

lungs segmentation without Mask image uisng Tradinational Computer vision (open-CV)

machine-learning-a-to-z

Linear Regression , logistic Regression , KNN, K Mean ,DecisionTrees_RandomForest_Classification,Feature Engineering,K fold,PCA,Random_Forest_Regression,RMSE

machine-learning-models

Debug machine learning classifiers and explain their predictions

manually-created-one-hot-encoding-

Manullay created Hot one encoding and find unique values form csv

multi-label-image-classification-in-keras

Classify the multi-­‐label images classification according to their given label . build the model from the scratch in keras

multi-label-image-classification-model-in-python

Multi-Label Image Classification Model in Python (Kearas)

multi-lable-classifiaction-on-tabluar-data-

This Project is for self learning Purpose

my-laptop-requirements-