Aditya Dutt's Projects
Classifying audio using Wavelet transform and deep learning
Awesome Knowledge-Distillation. εη±»ζ΄ηηη₯θ―θΈι¦paper(2014-2021)γ
π π A reading list focused on Multimodal Emotion Recognition (MER) ππ π π¬
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.
Classify bird's sound using siamese networks and few-shot learning.
Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.
Implementation of Bitcoin protocol to simulate bitcoin mining, wallet, and transactions.
The goal of this project is to implement the Chord protocol using the actor model in elixir
Dynamic digram is a small library that base on html5 canvas. it helps you to create flowchart as easy as possible.
The problem is to find perfect squares that are sums of consecutive squares. A classic example is the Pythagorean identity: 3^2 + 4^2 = 5^2
A list of papers for emotion recognition using machine learning/deep learning.
Folder / directory structure options and naming conventions for software projects
Implementation of gossip protocols for information dissemination in a network with different kinds of topologies.
Gumbel-Softmax Variational Autoencoder with Keras
A fast python library to detect loops, outer boundary and edges in binary images.
Demonstration of a few useful segmentation algorithms.
Task scheduler using RB tree and min heap.
This program demonstrates (i) the speedup obtained by using FFTs in numerical convolution. The two sequences x and y must contain at least 1000 elements each. The convolution code is written on own and libraries are used for the FFT computation. The speedup is documented using TIC TOC. (ii) The errors between circular convolution using FFTs and linear convolution (direct computation) is documented. In both (i) and (ii), 5 sets of random x and y sequences are used.
A small database to test different machine learning tasks. It contains simple shapes of different colors.
Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.
A demonstration of how to use PCA to see if data is linear or not
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Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent