shrejais Goto Github PK

2020

Materials for Applied Data Analysis CS-401, fall 2020

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation

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 “clean” 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.

ai-school

Various projects and examples for Artificial Intelligence. From Probabilistic Programming to Neural Networks.

all-of-statistics

Self-study on Larry Wasserman's "All of Statistics"

aml-xai-2

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.

apkit

Audio Processing Kit -- a python library

applied-deep-learning

Applied Deep Learning

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

artificial-intelligence-deep-learning-machine-learning-tutorials

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.

audiocoding_tutorials

Audio Coding Tutorials

awesome-diffusion-models

A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models

awesome-hacking

A collection of various awesome lists for hackers, pentesters and security researchers

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-meta-learning

A curated list of Meta-Learning resources/papers.

awesome-normalizing-flows

A list of awesome resources on normalizing flows.

awesome-rl

Reinforcement learning resources curated

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

awesome-semi-supervised-learning

:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

beanmachine

A library that allows for inference on probabilistic models

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

centernet2

Two-stage CenterNet

club

Code for ICML2020 paper - CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

cnbf

Complex Neural Beamformer

coding-interview-university

A complete computer science study plan to become a software engineer.

convnext

Code release for ConvNeXt model

cornell-cs5785-applied-ml

Teaching materials for the applied machine learning course at Cornell Tech

course-content-dl