junayed Goto Github PK

deep-learning-research

All my R&D Jupyter Notebooks regarding Deep Learning

deepjdot

Implementation of DeepJDOT in Keras

deeplearningtutorial

Deep Learning tutorial

deepsleepnet

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

deployment-ml-models-into-flask

discriminative-feature-alignment

Discriminative Feature Alignment for Unsupervised Domain Adaptation

dive-into-machine-learning

将日常学习使用的算法总结归类

domain-adaptation-internship

Repository to maintain code for my internship on Domain Adaptation

domain-adaptive-segmentation

Domain adaptation segmentation for volume EM imaging

dsn_fewshot

engie-wind-turbine-power-prediction

engie-wind-turbine-power-prediction

event-identification-earthquake-methods

This methods can be used for event identification to detect first arrival time of Vp and Vs. These methods are Akaike Information Criterion, MER and S/L kurt

explainable_artificial_intelligence

Slides, code and resources for model interpretation methods in machine learning and deep learning

f1-precision-and-recall-from-confusion-matrix---reverse

Sometimes it might happen that we considered only precision score from the computed model. We saved the confusion matrix for multiclass, and we have calculated the precision score. However, after some time, you might be needing to calculate the Recall, and F1 score from that confusion matrix. It is possible to calculate through the equations. You can easily see the description of precision, recall and F1 score from this link: https://en.wikipedia.org/wiki/Precision_and_recall. However, for multiclass classification, it is often very tough to back calculate all the things. So in this code, I have attempted to find out a way to calculate all the measurement paramters from the confusion matrix. You just need to provide the confusion matrix to the code, and it will calculate the rest for you.

fastai-lime

FastAI Model Interpretation with LIME

fastbook

Draft of the fastai book

feat

The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"

feature-engineering

feature-engineering-1

Tips for Advanced Feature Engineering

feature-engineering-and-feature-selection

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

feature-engineering-live-sessions

feature-selection-techniques

Python code source for features selection 👨‍🔬 series on medium website. 📰

feature_engineering_session_dhs18

Contains code and presentation for my interactive hack session, 'Effective Feature Engineering: A Structured Approach to Building Better Machine Learning Models' where we look at two interesting case studies on how to effectively leverage feature engineering and use a structured approach to build good machine learning models. This is created for the purpose of being presented in Analytics Vidhya's DataHack Summit 2018

femto

Spark - Bearing RUL Predictions

gifti

MATLAB GIfTI Library

gmmot

Python notebooks for Optimal Transport between Gaussian Mixture Models

gradcam_plus_plus-pytorch

A Simple pytorch implementation of GradCAM and GradCAM++

graph-signal-processing

graph2speak

Code associated with our paper "Graph2Speak: Improving Speaker Identification with Network Knowledge in Criminal Conversational Data"