rohitkt10 Goto Github PK

academic-kickstart

algos

Algorithms and Data Structures book

andy-hazell-code

Automatically exported from code.google.com/p/andy-hazell-code

as-data-sets

Active Subspace Data Sets

awesome-graph-explainability-papers

Papers about explainability of GNNs

bayesian_adaptation_vigeodmc

Contains the tool functions to implement the Bayesian methodology presented in the paper "Bayesian adaptation of chaos representations using variational inference and sampling on geodesics"

bda_course_aalto

Bayesian Data Analysis course at Aalto

bert

TensorFlow code and pre-trained models for BERT

black-tea.github.io

My simple website

cipher

codes

cookbook

The Data Engineering Cookbook

deep-uq-paper

A repository that contains scripts to replicate results in the Deep UQ paper.

deepkoopman

neural networks to learn Koopman eigenfunctions

deeplearningbook

MIT Deep Learning Book in PDF format

exponential_activations

"Improving representations of genomic sequence motifs in convolutional networks with exponential activations" by Koo and Ploenzke (https://doi.org/10.1101/2020.06.14.150706)

gpml_implement

Python Implementation of Algorithms in the Rasmussen and Williams' book on Gaussian Processes in Machine Learning

granular-chains

The first version

graphicalmodel

Courera Version of Graphical Model.. Cooperate with Jian Guo.

higher_order_conv

homework_2020

Homeworks

ipython

jaxbd2k-shortcourse

This repository contains course materials from JAX-BD2K workshop.

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

knowledge-distillation-zoo

Pytorch implementation of various Knowledge Distillation (KD) methods.

knowledge_distillation

lab_2020

Repository for all labs for STAT115 for 2020

learning_sequence_motifs

"Representation Learning of Genomic Sequence Motifs with Convolutional Neural Networks" by Peter K. Koo and Sean R. Eddy

mapping

Quantitative Trait Mapping Lesson

mlfs-book

O'Reilly book - Building Machine Learning Systems with a feature store: batch, real-time, and LLMs