omidbazgirttu Goto Github PK

algorithm_datastructure

In this repository I keep my practices on Algorithm and Data structure, based on Coursera and LeetCode trainings and problems.

algorithms

Fundamental of Algorithms

baikal

A graph-based functional API for building complex scikit-learn pipelines.

bayesian-optimization-from-scratch

In this repository I am gonna practice Bayesian optimization from scratch for machine learning models' hyper-parameter search

bayesianlearing

Here I keep my practices and codes on bayesian statistics and bayesian learning. It is mainly for my own learning purpose, but I comment my code, so if somebody finds them useful, would be able to understand them.

bayesianlearning

bioinformatics

Here I keep the codes that I write for my bioinformatics course.

cancer_mdpi

chatdoc

ci_cd

CI_CD practices

codingpractice

I practice coding through hacker rank, and keeping my solutions here for my own sake.

covid19

Modeling COVID19 clinical data using REFINED approach.

czbiohub

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

generative_models

In this repository I keep my codes on learning about GANs and generative models mostly. The GANs code are mostly from the Coursera course on GANs.

graph-machine-learning

Graph Machine Learning Training

hyperas

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

hyperopt-keras-cnn-cifar-100

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task. Monitoring with TensorBoard. Other visualizations available.

hyperopt_fashionmnist

# Bayesian Optimization In this example a bayesian framework is defined to tune hyperparameter of a CNN using hyperopt library developed https://github.com/hyperopt Bayesian optimization is a seuential model-based approach to solving problems. In particular, we prescribe a prior belief over the possible objective functions and then sequentially refine this model as data are observed via our updated beliefs-given data-on the likely ojective function we are optimizing. https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf This blog summarises bayesian optimization very thoroughly. https://medium.com/vantageai/bringing-back-the-time-spent-on-hyperparameter-tuning-with-bayesian-optimisation-2e21a3198afb The CNN is used to model fashion MNIST dataset.

integratedrefined

In this repository the code that ensemble different REFINED approaches is provided.

kmm

Kernel Mean Matching implementation

llm101n

LLM101n: Let's build a Storyteller

llms

LLM and GPT from scratch

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

ml-university

Machine Learning Open Source University

mlops

Github repository of my learnings on MLOps starting with #duke-mlops-python

mpi-coding

In this repository I keep some of my MPI coding to follow them later on and also share them with the python community. I do deep learning on drug sensitivity prediction, so my main purpose is making all the DL codes applied on different genes/drugs/cell lines in parallel.

mr-spectra-baseline-correction

The baseline correction code of the https://ieeexplore.ieee.org/document/8470319

mzsr

Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)

omid-takehome