Inherently Interpretable Time Series Classification via Multiple Instance Learning (MILLET)

Minimal and clean examples of machine learning algorithms implementations

Code of Sub-SpaCE to be presented in xAI-2024

A repository to prepare you for your machine learning interview, involving most of the questions asked by all the tech giants and local companies. Do this to Ace your Machine Learning Engineer Inte…

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

"TSEvo: Counterfactuals for Time Series Classification" accepted at ICMLA '22.

An Open-Source Library for the interpretability of time series classifiers

This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series.…

Multivariate LSTM Fully Convolutional Networks for Time Series Classification

A middle-to-high level open source algorithm book designed with coding interview at heart!

1000 images, one per image-net class. For easy visualization/exploration of classes.

A Pytorch Implementation of a denoising autoencoder.

PyTorch Implementation of Physics-informed Neural Networks

Physics-guided Neural Networks (PGNN) : An Application In Lake Temperature Modelling

Python tool for downloading, converting and reading OMNI solar wind data.

Implementation of the node2vec algorithm.

This is a collection of resources and notes for you to land your first software engineer job

Python package for sequence (e.g. trend line, sentence, image) clustering

Deep Learning Specialization by Andrew Ng on Coursera.

deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link

A resource for learning about Machine learning & Deep Learning

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

Synthetic data generators for tabular and time-series data