Sarabjot Singh 's starred repositories
awesome-project-ideas
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
image-segmentation-keras
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
EffectivePyTorch
PyTorch tutorials and best practices.
pytorch-mobilenet-v2
A PyTorch implementation of MobileNet V2 architecture and pretrained model.
Awesome-Speech-Enhancement
A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.
ResidualAttentionNetwork-pytorch
a pytorch code about Residual Attention Network. This code is based on two projects from
vit-tensorflow
Vision Transformer Cookbook with Tensorflow
Transformers-for-NLP-and-Computer-Vision-3rd-Edition
Transformers 3rd Edition
deep-listening
Deep Learning experiments for audio classification
residual_attention_network
Keras implementation of Residual Attention Network
ResidualAttentionNetwork
A Gluon implement of Residual Attention Network. Best acc on cifar10-97.78%.
DeepAudioClassification
Audio classification using Tensorflow
Residual-Attention-Network
Keras Implementation Residual Attention Network
CLIP-Tensorflow
Simple Tensorflow implementation of CLIP
audio_classification
Multi-class audio classification with MFCC features using CNN
Audio-Classification-using-Deep-Learning
Classifying 10 different categories of Sound using Deep Learning.
gender-audio-classification
A speaker gender classifier. MFC feature engineering and a pre-trained ResNet-50. GradCAM interpretation.
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
researchpapers
A list of research papers that I have read so far and recommend to other Deep Learning enthusiasts such as myself.
ResNet-Attention-layer-custom-data-set
This is a deep learning network: ResNet with an attention layer that can be used on a custom data set.
keras-applications
Reference implementations of popular deep learning models.