mahmudrahman Goto Github PK

amunategui.github.io

api-samples

Code samples for YouTube APIs, including the YouTube Data API, YouTube Analytics API, and YouTube Live Streaming API. The repo contains language-specific directories that contain the samples.

bdl-benchmarks

Bayesian Deep Learning Benchmarks

benchmarking_dl_mimiciii

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

cmprsk

Regression modeling of sub-distribution functions in competing risks

coursera-deep-learning

Solutions to all quiz and all the programming assignments!!!

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cox-nnet

coxph.risk

Absolute risk estimation based on Cox proportional hazards models for the primary and competing events

d2l-en

Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface.

datacamp

DataCamp data-science courses

datasciencecoursera

datasharing

The Leek group guide to data sharing

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep_learning_book

হাতেকলমে ডীপ লার্নিং

deepbayes-2019

Practical assignments of the Deep|Bayes summer school 2019

deephit

DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks

deeplearningzerotoall

TensorFlow Basic Tutorial Labs

dnnsurv

drsa

Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper.

ensemble-methods-using-r

I have done my individual project (dissertation) on ensemble methods. In which I first did the background study on different ensemble methods and then implemented Boosting, AdaBoost, Bagging and random forest techniques on underlying machine learning algorithms. I used boosting method to boost the performance of weak learner like decision stumps. Implemented bagging for decision trees (both regression and classification problems) and for KNN classifier. Used random forest for classification trees. I have implemented a special algorithm of boosting called “AdaBoost” on logistic regression algorithm using different threshold values. Then plotted the different graphs like an error rate as a function of boosting, bagging and random forest iterations. Compared results of bagging with boosting. Analysed the performance of classifier before applying ensemble methods and after applying ensemble methods. Used different model evaluation techniques like cross-validation, MSE, PRSS, ROC curves, confusion matrix, and out-of-bag error estimation to estimate the performance of ensemble techniques.

esp

ESP (Early Stage Prediction) for Longitudinal Data

image-completion-using-mixture-of-bernoullis

csc321 PA4

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

kaggle-competition-influencers-in-social-networks-twitter-dataset

A machine learning project to predict who's more influential in Twitter. Project involved experimentation with various machine algorithms such as decision trees, logistic regression, support vector machines(SVM), random forests and gradient boosting machine(GBM)

lifetimerisk

Lifetime risk prediction model with pseudo-observations

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

mahmud

matchingweight

Matching Weight Propensity Scoring