angadbajwa23

angadbajwa23's Stars

• donnemartin/system-design-primer

  Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

  Language:Python287k6.7k31647.8k

• DopplerHQ/awesome-interview-questions

  :octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:

  72.8k1.7k629k

• TencentARC/GFPGAN

  GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

  Language:Python36.2k5074796k

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.7k2211584.1k

• janishar/mit-deep-learning-book-pdf

  MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

  Language:Java13.1k411182.7k

• bnsreenu/python_for_microscopists

  https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

  Language:Jupyter Notebook4.1k113752.4k

• riti2409/Resources-for-preparation-Of-Placements

  Lecture video links for preparation of Placements

  3.6k4211904

• heykeetae/Self-Attention-GAN

  Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

  Language:Python2.5k3565476

• nitinkaushik01/Deep_and_Machine_Learning_Projects

  This Repository contains the list of various Machine and Deep Learning related projects. Related code and data files are available inside this folder. One can go through these projects to implement them in real life for specific use cases.

  Language:Jupyter Notebook570226368

• CMU-HKN/CMU-ECE-CS-Guide

  How to survive CMU as an ECE/CS major

  532138131

• pratikratadiya/awesome-ml-internships

  List of companies offering Machine learning and Data Science internships

  45321040

• mortezamg63/Accessing-and-modifying-different-layers-of-a-pretrained-model-in-pytorch

  3512361

• tejasrsuthar/Awesome-NodeJS

  A curated collection of best NodeJS Resources ✍️

  Language:MDX33614069

• anuj-glitch/Disease-Prediction-using-Django-and-machine-learning

  **A end to end project - Powered by Django and Machine Learning** - This project aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases and consult to the doctor online.

  Language:CSS27361880

• jiankang1991/road_extraction_remote_sensing

  Road Extraction based on U-Net architecture (CVPR2018 DeepGlobe Challenge submission)

  Language:Python813522

• kenza-bouzid/TransUnet

  This repo reproduces the results of TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

  Language:Jupyter Notebook771618

• menon92/WaveletCNN

  Wavelet CNN, Texture Classification in Keras

  Language:Jupyter Notebook514118

• KingArthur000/Electrical-Fault-detection-and-classification

  A sample power system was modeled using MATLAB Simulink and all six types of faults were introduced into the transmission line of the power system. An ML classifier using 8 types model was implemented using sklearn and an appropriate model was selected as the end model for each problem.

  Language:Jupyter Notebook441118

• papercodeIN/OpenOPC-PyModbus-Snap7-MQTT

  Language:Jupyter Notebook434029

• interpss/DeepMachineLearning

  Language:Java36181021

• AgHarsh/Fault-Detection-in-Power-Microgrid

  This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. The electronic equipment used in microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. A fault occurs when two or more conductors come in contact with each other or ground. Ground faults are considered as one of the main problems in power systems and account for more than 80% of all faults. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. In this project we worked on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network.

  Language:Jupyter Notebook341012

• ulaval-damas/tree-bark-classification

  Language:Python281167

• alfianhid/Feature-Extraction-Gray-Level-Co-occurrence-Matrix-GLCM-with-Python

  Language:Jupyter Notebook24105

• chachkes247/Power-Quality-Disturbances

  209

• sarajcev/PSS-ML

  Machine learning for power system stability analysis

  Language:Jupyter Notebook17332

• thunderInfy/simclr

  This repo contains code for SimCLR : A Simple Framework for Contrastive Learning of Visual Representations

  Language:Python17338

• upashu1/Pytorch-UNet-2

  UNet 2.0 - Pytorch implementation of the U-Net 2.0 for image semantic segmentation, with processing blocks for noisy images

  Language:Python8105

• Minauras/road-segmentation

  :mortar_board: ML project for EPFL course CS-433 Machine Learning. Comparing ResNet and UNET for a road segmentation task.

  Language:Python5101

• pratikratadiya/Toxicity_detection

  Detection of toxicity in a given comment using various sequence models

  Language:Jupyter Notebook4011

• leviettuan/GLBP

  Gradient Local Binary Pattern

  110