Absar-Qadri

Absar-Qadri's Stars

• usmanrangrez/SWIGGY_CLONE

  A Clone of Swiggy which has numerous features like accordion,higher order componet usage,Offline Online,Login system using custom hook and much more!If the Data is Not rendering at initial load),please install ALoow CORS Extension. --https://chromewebstore.google.com/detail/allow-cors-access-control/lhobafahddgcelffkeicbaginigeejlf

  Language:JavaScript2

• usmanrangrez/Github-App

  Check anyone profile on Github thier most topped (stars) repos fork count and much more!

  Language:JavaScript31

• Absar-Qadri/PERSONAL-PORTFOLIO

  Heyy! Check out my portfolio using HTML,CSS AND JS.

  Language:HTML1

• usmanrangrez/OLD-PORTFOLIO

  Heyy guys. This is my first Portfolio project using pure CSS

  Language:HTML1

• usmanrangrez/LGVIMP-Web

  TASK 1

  Language:CSS1

• usmanrangrez/ROLL-YOUR-DICE

  Hey Guys!. Check out my game written in HTMl,CSS and JS.. Roll you dice and get your score to 100 to win.

  Language:JavaScript2

• atulya-deep/Boon.com

  SEP project

  2

• atulya-deep/CASINO-ROYAL

  A CASINO GAME MADE IN C LANGUAGE

  Language:C21

• atulya-deep/SUVEN-INTERNSHIP

  WEB DEVELOPMENT INTERNSHIP WITH HTML,CSS,JAVA SCRIPT,BOOTSTRAP

  Language:HTML21

• atulya-deep/Deep-Learning-Workshop---Furniture-Detection-1

  Furniture Detection using YOLOv5

  Language:Python2

• atulya-deep/atulya-deep

  Config files for my GitHub profile.

  21

• atulya-deep/Sigmoid-neuron-for-classification-problem

  Gender classification using Sigmoid neuron

  Language:Python2

• atulya-deep/Naive--bayes-to-solve-a-Multi--class-classification-problem

  Naive-Bayes classifier to solve a Multi- class classification problem

  Language:Python2

• atulya-deep/Object-dectection-using-YOLO

  YOLO is an acronym that stands for You Only Look Once. We are employing Version 5, which was launched by Ultralytics in June 2020 and is now the most advanced object identification algorithm available. It is a novel convolutional neural network (CNN) that detects objects in real-time with great accuracy. This approach uses a single neural network to process the entire picture, then separates it into parts and predicts bounding boxes and probabilities for each component. These bounding boxes are weighted by the expected probability. The method “just looks once” at the image in the sense that it makes predictions after only one forward propagation run through the neural network. It then delivers detected items after non-max suppression (which ensures that the object detection algorithm only identifies each object once)

  Language:Python31

• atulya-deep/Neural-Network-for-Multiclass-classification

  Language:Python2

• atulya-deep/Linear-regression-with-stochastic-gradient-descent

  Implement linear regression with (i) stochastic gradient descent. and (ii) stochastic mini-batch gradient descent. Comparing the results

  Language:Python3

• atulya-deep/Attendance-system-using-Facial-Recognition

  Language:Python11

• atulya-deep/Optimizing-neural-networks

  Optimizing neural networks using L2 regularization, Dropout, data augmentation and early stopping.

  Language:Python2

• atulya-deep/Image-classification-transfer-learning

  Transfer learning implementation using VGG16 model to classify images

  Language:Jupyter Notebook2

• atulya-deep/Image-classification

  Using Le net and ResNet

  Language:Python3

• atulya-deep/RNN-for-Character-level-language-modeling

  Language:Jupyter Notebook2

• atulya-deep/LSTM-network-for-Named-Entity-recognition

  LSTM network for Named Entity recognition

  Language:Jupyter Notebook3