Name: Abdullah Y Muaad
Type: User
Company: University of Mysuru
Bio: Abdullah Yahya Mohammed Muaad is a student in University of Mysore, India. He received his master’s degree in from the University of Mysore, India
Location: Mysore ,Karnataka,India
Abdullah Y Muaad's Projects
Config files for my GitHub profile.
The project is created for an autonomous vehicle which identifies road area in an image. The source code requires OpenCV and works with arduino
This repo will house all our course material and code snippets from the Introduction to Machine Learning Class
Lab Materials for MIT 6.S191: Introduction to Deep Learning
This repo represents model developed for Irony and sentiment detection in Arabic tweets in WANLP shared tasks on sarcasm and sentiment detection in Arabic tweets
Collection of Jupyter Notebooks demoed on https://www.youtube.com/stevesiedata
Deep Learning for humans
中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN
k
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay
Experiments with long sentences Transformers methods (LongFormer and Reformer)
Longformer: The Long-Document Transformer
Documentations of Machine Learning & Natural Language Processing concepts and algorithms
Code Repository for Machine Learning with PyTorch and Scikit-Learn
This repository contains all resources (code, notebooks,etc) used for my Medium blog page.
Companion webpage to the book "Mathematics For Machine Learning"
Money Laundering Detection Using Machine Learning
Multi Label text classification using bert
(Multiclass Arabic Text classification Using Keras)
Multiclass Segmentation using UNET on Crowd Instance-level Human Parsing (CHIP) dataset