Title: Practical MLOps: Operationalizing Machine Learning Models, Author: Noah Gift
Title: Machine Learning Projects for .NET Developers / Edition 1, Author: Mathias Brandewinder
Title: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Author: Aur lien G ron
Title: Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk, Author: Abdullah Karasan
Title: Practical Weak Supervision: Doing More with Less Data, Author: Wee Hyong Tok
Title: Foundations of Inductive Logic Programming / Edition 1, Author: Shan-Hwei Nienhuys-Cheng
Title: Introduction to Machine Learning and Bioinformatics / Edition 1, Author: Sushmita Mitra
Title: Probabilistic Approaches for Social Media Analysis, Author: Yin Li Wu Liu & Zidu Yin & Zidu Kun Yue
Title: The Mathematics Of Generalization / Edition 1, Author: David. H Wolpert
Title: Meta-Learning: Strategies, Implementations, and Evaluations for Algorithm Selection: Volume 91 Dissertation in Artificial Intelligence, Author: Christian Rudolf Kopf
Title: Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch, Author: Adi Polak
Title: Managing Cloud Native Data on Kubernetes: Architecting Cloud Native Data Services Using Open Source Technology, Author: Jeff Carpenter
Title: Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow, Author: Hannes Hapke
Title: Reliable Machine Learning: Applying SRE Principles to ML in Production, Author: Cathy Chen
Title: Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise, Author: Daniel Vaughan
Title: Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, Author: Jeremy Howard
Title: Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines, Author: Yada Pruksachatkun
Title: The Kaggle Book: Data analysis and machine learning for competitive data science, Author: Konrad Banachewicz
Title: Machine Learning for High-Risk Applications: Approaches to Responsible AI, Author: Patrick Hall
Title: AI at the Edge: Solving Real-World Problems with Embedded Machine Learning, Author: Daniel Situnayake

Pagination Links