Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images

PyTorch implementation of IndRNN with custom C++/CUDA extensions

C++ Implementation of PyTorch Tutorials for Everyone

Generating quote-like text with Recurrent Neural Networks (RNNs)

Exploring the intersection of supervised machine learning algorithms and weather data to drive ClimateWins forward. (CF student project)

This notebook loads a time series of gas concentrations in the air to train a recurrent neural network to predict the next hour of data

For Storing Valuable Snippets from Videos with I Featuers -> ✂️ Trimming, ✍🏻Notes Section, 🗃️Storing (Folder-Style) , 📝Transcript Exporting, 👾AI-Chatbot with acces to video transcript.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Network…

Developed a recurrent neural network for sentimental analysis to check whether the user have positive, neutral or negative sentiment

Speech --> Text - 🧠Recurrent Neural Network

Tinymind is a Neural Network and Machine Learning project intended to provide a C++ template library for neural nets and machine learning algorithms within embedded systems.

Codes for training and analyzing RNNs to perform long-memory tasks with different curricula.

Deep Learning in python

Implementation of Liquid Nets in Pytorch

This repo contains my work & The code base for this Deep Learning Specialization offered by deeplearning.AI

We build a neural network from scratch to be trained on the MNIST dataset. We also use PyTorch on a London weather dataset for forecasting daily mean temperatures.

Book of Demythologize Durbin-Watson Test Statistic | Correct the critical values of DW statistic

[IEEE TGRS 2020] Official Tensorflow implementation for Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

Participants in this Specialization have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. They learn to enhance these networks with techniques such as Dropout, BatchNorm, Xavier/He initialization, among others.

Exercise 3 for Deep Learning Course at FAU