sandipank Goto Github PK

applied_data_science

autograd

Efficiently computes derivatives of numpy code.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

bios8366

Advanced Statistical Computing at Vanderbilt University's Department of Biostatistics

bohemianflautist

Sandipan Karmakar's Webpage

computational-statistics-with-python

Resources for STA 633 class

cookbook-code

Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python

data-analysis

Data Science Using Python

dataconfs

A list of conferences connected with data worldwide.

deep-learning-book

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

deeplearning-models

A collection of various deep learning architectures, models, and tips

ggplot

ggplot for python

healthcare

Insurance cost predictor

hyperparameter-optimization

Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms

jekyll

:globe_with_meridians: Jekyll is a blog-aware, static site generator in Ruby

juliacoursenotebooks

Jupyter notebooks for the Julia Scientific Programming course on Coursera

mcmc-log-reg

Contains a class that fits logistic regression parameters via a modified Metropolis-Hastings algorithm.

ml-classification

Codes from Coursera's course Machine Learning - classification, part of machine learning specialization offered by University of Washington

ml-clustering

Codes from Coursera's course Machine Learning - clustering, part of machine learning specialization offered by University of Washington

ml-foundation

Codes from Coursera's course Machine Learning - foundation, part of machine learning specialization offered by University of Washington

ml-regression

Codes from Coursera's course Machine Learning - regression, part of machine learning specialization offered by University of Washington

mlr

mlr: Machine Learning in R

neural-networks-for-machine-learning

Programming assignment given in course 'Neural Networks for machine learning' taught by Prof. Geoffrey Hinton, University of Toronto.

notebook

Collection of jupyter notebooks for demonstrating software.

prml

Pattern recognition and machine learning toolbox

prml-python-toolbox

Python implementation of the famous PRML book by C M Bishop

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

pymlslides

Slides for my machine learning course based on Sebastian Raschka's Python Machine Learning book

pyprobml

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource