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autokeras

AutoML library for deep learning

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

bsrt

Pytorch code for "BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment", CVPRW, 1st place in NTIRE 2022 BurstSR Challenge (real-world track).

climate-learn

Source code for ClimateLearn

dash-predictive-maintenance

Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

libsvm

lofo-importance

Leave One Feature Out Importance

mindsdb

An emerging platform to help developers build #AI solutions

mlxtend

A library of extension and helper modules for Python's data analysis and machine learning libraries.

octave

my octave exercises for 2011 stanford machine learning class, posted after the due date of course

optuna-examples

Examples for https://github.com/optuna/optuna

physo

Physical Symbolic Optimization

piml-toolbox

PiML (Python Interpretable Machine Learning) toolbox for model development and model validation

practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

prml

Pattern recognition and machine learning toolbox

prompt-engineering-guide

:octopus: Guides, papers, lecture, and resources for prompt engineering

symbolicai

Compositional Differentiable Programming Library

thundersvm

ThunderSVM: A Fast SVM Library on GPUs and CPUs

transfer-learning-for-nlp

Companion repository to Paul Azunre's "Transfer Learning for Natural Language Processing" book

transformers

wt_fault_identification

Wind Turbine Fault Identification using Machine Learning Techniques applied to SCADA data

wtphm

SCADA data pre-processing library for prognostics, health management and fault detection of wind turbines. Successor to https://github.com/lkev/wt-fdd