-predict-future-sales

ankur-machine-learning-

Client: M17(https://m17.asia/en/product/17media/) a streaming platform. DataLink: ​ Client_data.zip The attached data here is generated from the live-streaming platform. Analyze the data to come up with the top 20% streamers. Using these top 20% streamers as good streamers create a classification model which can classify whether any streamer is good streamer or not. Evaluation Metric​ : F1-Score Classification Report (Using sklearn.metrics.classification_report) is also required

autoencoder-cifar10.py-using-keras.ipynb

automated-image-classification

This Repo is for self learning purpose

build-a-restful-service-that-extracts-expense-date-from-a-receipt.

Build a RESTful service that extracts expense date from a receipt.Deploy the service on any cloud platform like Heroku/AWS/GCP. The service should contain one API which has the following contract: Request: POST /extract_date Payload: {“base_64_image_content”: <base_64_image_content>} Response: If date is present: {“date”: “YYYY-MM-DD”} If date is not present: {“date”: null}

busigence-assigment-multilable-image-classifictation-in-keras

calculate-the-screen-time-of-video-using-cnn

Calculate the Screen Time of Actors in any Video (with Python codes) using Convolutional Neural Networks

cat-and-dog

cat-and-dog-nitin

this repo is for self learning purpose

chest-x-ray-images-pneumonia-

using Fast.ai

cifar-10-data-set-with-train-accuracy-99-and-test-accuracy-93-

This repo is for self learning purpose

cnn-code-for-raw-image-

cnn code in pytorch for raw colour image with accuracy of 96.2 %

cnn-code-in-pytorch

CNN code in pytorch for raw image in Jupiter notebook with accuracy of 96.2%

coding-

python basic A to Z

comaptaring-two-columns-

This repo is for self learning purpose for comparing two numerical columns and creating a new columns for saving higher have from two colomns

conv2d-with-tabular-data-

This repo is for self learning Purpose

converting-image-into-base-64-and-coverting-base64-to-image

This repo is for self learning purpose

converting-rbg-image-to-gray-scale

converting and resize colour image into gray scale using opencv

covid-19-time-series-classification

This repo is for self learning purpose

dentisty.ai

Please use the following assignment for Data Scientist position: The deadline of Assignment is 3 days. 1. Download the data-set from : https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/ 2. Augment the data-set to make it efficient against shift, light variation and other noise. 3. Train a transfer learning based classifier to classify images according to benign/malignant given in the Excel file. 4. Train a transfer learning based Semantic segmentation according to the annotation in _anno.BMP file. 5. Upload the assignment on your Github in form of a IPYNB showing results on a few test set images.

develop-a-generalized-algorithm-to-detect-the-brightness-of-any-image

Develop a generalized algorithm to detect the brightness of any image. Your algorithm should take an image as input and give a score between (0-10) as output (zero being low bright and 10 being high bright). You will not be provided with data for training.

docker-nlm-

This is my first docker image this repo is for personal learning purpose

exp

fast.ai

Multi label image classification problem

faster-r-cnn-for-open-images-dataset-by-keras

Introduction The original code of Keras version of Faster R-CNN I used was written by yhenon (resource link: GitHub .) He used the PASCAL VOC 2007, 2012, and MS COCO datasets. For me, I just extracted three classes, “Person”, “Car” and “Mobile phone”, from Google’s Open Images Dataset V4. I applied configs different from his work to fit my dataset and I removed unuseful code. Btw, to run this on Google Colab (for free GPU computing up to 12hrs), I compressed all the code into three .ipynb notebooks. Sorry for the messy structure. I wrote my exploring and experiment results for Faster R-CNN in this article in Medium. If you are in China, you cannot directly access Medium. So I make a copy in here. Project Structure Object_Detection_DataPreprocessing.ipynb is the file to extract subdata from Open Images Dataset V4 which includes downloading the images and creating the annotation files for our training. I run this part by my own computer because of no need for GPU computation. frcnn_train_vgg.ipynb is the file to train the model. The configuration and model saved path are inside this file. frcnn_test_vgg.ipynb is the file to test the model with test images and calculate the mAP (mean average precision) for the model.

faster-r-cnn-model-deployment-using-flask-on-local-host

In this Repository I have deployed the faster Rcnn model on flask to deploy it on local server and aslo provided the code for how to use AWS sagamaker to deploy your pre train model on aws

faster-r-cnn-model-dockerization

faster-rcnn-docker-

fine-tune-vgg16-image-classifier-with-keras

Fine-tune VGG16 Image Classifier with Keras

fine-tune-vgg16-image-classifier-with-keras-

Fine-tune VGG16 Image Classifier with Keras