Machine learning for societal improvement, modernization, and progress /

Grokking Machine Learning, video edition /
by: Serrano, Luis G.
Published: (2021)

Machine learning : ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002 : proceedings /
Published: (2002)

Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale.
by: Kumar M. J., Jayanth
Published: (2022)

Machine learning : ECML 2003 : 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003 : proceedings /
Published: (2003)

Applied Machine Learning Explainability Techniques : Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More /
by: Bhattacharya, Aditya
Published: (2022)

What developers need to know to design machine learning systems.
Published: (2022)

Grokking Machine Learning.
by: Serrano, Luis G.
Published: (2021)

Machine learning bookcamp /
by: Grigoriev, Alexey
Published: (2021)

Machine learning : hands-on for developers and technical professionals /
by: Bell, Jason (Computer scientist)
Published: (2020)

Ensemble machine learning techniques /
Published: (2018)

Design Patterns für Machine Learning /
by: Lakshmanan, Valliappa, et al.
Published: (2021)

Data science and machine learning : (theory and projects) A to Z.
Published: (2021)

Introduction to machine learning /
by: Alpaydin, Ethem
Published: (2014)

Fundamentals and methods of machine and deep learning : algorithms, tools and applications /
Published: (2022)

Machine learning applications in electromagnetics and antenna array processing /
by: Martínez-Ramón, Manel, 1968-
Published: (2021)

Machine learning : ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001 : proceedings /
Published: (2001)

Machine learning : ECML 2000 : 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31-June 2, 2000 /
Published: (2000)

Practical Deep Learning at Scale with MLflow : Bridge the Gap Between Offline Experimentation and Online Production /
by: Liu, Yong
Published: (2022)

Learning from data : a short course /
by: Abu-Mostafa, Yaser S., 1957-
Published: (2012)

AZURE MACHINE LEARNING ENGINEERING : deploy, fine -tune and optimize ml models using microsoft azure /
by: Fakhraee, Sina
Published: (2022)

Deep learning /
by: Goodfellow, Ian, et al.
Published: (2016)

Ji qi xue xi : gong zuo xian chang de ping gu, dao ru yu shi zuo = Deep learning /
by: Ariga, Michiaki, et al.
Published: (2018)

Automated machine learning on AWS : fast-track the development of your production-ready machine learning applications the AWS way /
by: Potgieter, Trenton, et al.
Published: (2022)

TensorFlow.js model training.
Published: (2021)

AI Superstream.
Published: (2022)

Gradient boosting from scratch.
Published: (2020)

MLOps key concepts.
Published: (2022)

Explainable AI for Practitioners : designing and implementing explainable ML solutions /
by: Munn, Michael (ML solutions engineer)
Published: (2022)

MLOps masterclass : theory to DevOps to Cloud-native to AutoML /
Published: (2022)

Conda commands for beginners.
Published: (2022)

Deep learning with PyTorch Lightning : build and train high-performance artificial intelligence and self-supervised models using Python /
by: Sawarkar, Kunal, et al.
Published: (2021)

Machine learning engineering in action /
by: Wilson, Ben (Computer engineer)
Published: (2022)

Machine learning engineering in action /
by: Wilson, Ben (Computer engineer)
Published: (2022)

Genetic algorithms for machine learning /
Published: (1994)

Using lightning and hangar with PyTorch to reduce coding in deep learning projects.
Published: (2020)

Machine Learning at Scale with H2O : a Practical Guide to Building and Deploying Machine Learning Models on Enterprise Systems /
by: Keys, Gregory
Published: (2022)