Pinned Repositories
airflow-tutorial
Airflow basics tutorial
arogozhnikov.github.io
'Brilliantly wrong' blog, Machine Learning visualizations live here
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
awesome-vector-database
A curated list of awesome works related to high dimensional structure/vector search & database
Collaborative-Deep-Learning-for-Recommender-Systems
The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset. The resluts are summarized in "collaborative-deep-learning.pdf".
deep-bandits-TS-tutorial
linear contextual Thompson Sampling, deep NN contextual Thompson Sampling
deep-learning-keras-tensorflow
# Deep Learning with Keras and Tensorflow
iterative-proportional-fitting
Iterative Proportional fitting technique to create sample weights such that data is representative of the target dataset or distributions. In statistics, a sample statistic would be biased if sample is not representative of the "population". In ML/AB context, impact of an treatment in an experiment (treatment vs control) or impact of an attribute in an "explainable" model would be biased/incorrect if the distribution of the sample on features is not accurate.
MultiObj_MultiArmBandits
Notebooks explaining Multi objective optimisation with Pareto Frontier, and how to do Multi Arm Bandit Thompson Sampling with multiple objectives (Pareto-MOMAB)
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs
nm-narasimha's Repositories
nm-narasimha/deep-bandits-TS-tutorial
linear contextual Thompson Sampling, deep NN contextual Thompson Sampling
nm-narasimha/airflow-tutorial
Airflow basics tutorial
nm-narasimha/arogozhnikov.github.io
'Brilliantly wrong' blog, Machine Learning visualizations live here
nm-narasimha/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
nm-narasimha/awesome-vector-database
A curated list of awesome works related to high dimensional structure/vector search & database
nm-narasimha/Collaborative-Deep-Learning-for-Recommender-Systems
The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset. The resluts are summarized in "collaborative-deep-learning.pdf".
nm-narasimha/deep-learning-keras-tensorflow
# Deep Learning with Keras and Tensorflow
nm-narasimha/iterative-proportional-fitting
Iterative Proportional fitting technique to create sample weights such that data is representative of the target dataset or distributions. In statistics, a sample statistic would be biased if sample is not representative of the "population". In ML/AB context, impact of an treatment in an experiment (treatment vs control) or impact of an attribute in an "explainable" model would be biased/incorrect if the distribution of the sample on features is not accurate.
nm-narasimha/MultiObj_MultiArmBandits
Notebooks explaining Multi objective optimisation with Pareto Frontier, and how to do Multi Arm Bandit Thompson Sampling with multiple objectives (Pareto-MOMAB)
nm-narasimha/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs
nm-narasimha/Easy-deep-learning-with-Keras
Keras tutorial for beginners (using TF backend)
nm-narasimha/Kalman-and-Bayesian-Filters-in-Python
Forked from Rlabbe master branch in July 2023 - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
nm-narasimha/Keras-CDL
Keras implementation of Collaborative Deep Learning
nm-narasimha/Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
nm-narasimha/nips2016
A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2016
nm-narasimha/ODSC_2019-Multi-Modal-Learning
ODSC 2019 Workshop : https://confengine.com/odsc-india-2019/proposal/9276/person-identification-via-multi-modal-interface-with-combination-of-speech-and-image-data
nm-narasimha/practise1
babysteps
nm-narasimha/pretrained.ml
Compilation of pre-trained deep learning models with demos and code.
nm-narasimha/Python-WebCrawler
A web crawler written in Python
nm-narasimha/tensorflow
Computation using data flow graphs for scalable machine learning
nm-narasimha/titanic
nm-narasimha/TopDeepLearning
A list of popular github projects related to deep learning
nm-narasimha/vector-search-class-notes
Class notes for the course "Long Term Memory in AI - Vector Search and Databases" COS 597A @ Princeton Fall 2023