- π Hi, Iβm Shreyas Jaiswal.
- π Iβm interested in ..
- π± Iβm currently learning ...
- ποΈ Iβm looking to collaborate on ...
- π« How to reach me ...
shrejais Goto Github PK
Name: Shreyas Jaiswal
Type: User
Name: Shreyas Jaiswal
Type: User
Materials for Applied Data Analysis CS-401, fall 2020
500 AI Machine learning Deep learning Computer vision NLP Projects with code
We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the βcleanβ time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.
Various projects and examples for Artificial Intelligence. From Probabilistic Programming to Neural Networks.
Self-study on Larry Wasserman's "All of Statistics"
Exploring the creation of explainable AI for the task of Writer verification. Experimenting with techniques such as Probabilistic Graphical Models, Autoencoders, Siamese Networks, and Multi-Task Learning. Probabilistic Graphical modelling involves entropy learning and Sigmoid Structured CPD inference mechanism.
Audio Processing Kit -- a python library
Applied Deep Learning
π Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
Audio Coding Tutorials
A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models
A collection of various awesome lists for hackers, pentesters and security researchers
A curated list of awesome machine learning interpretability resources.
A curated list of Meta-Learning resources/papers.
A list of awesome resources on normalizing flows.
Reinforcement learning resources curated
:metal: awesome-semantic-segmentation
:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
Notebooks about Bayesian methods for machine learning
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
A library that allows for inference on probabilistic models
Python code for part 2 of the book Causal Inference: What If, by Miguel HernΓ‘n and James Robins
Two-stage CenterNet
Code for ICML2020 paper - CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Complex Neural Beamformer
A complete computer science study plan to become a software engineer.
Code release for ConvNeXt model
Teaching materials for the applied machine learning course at Cornell Tech
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.