bayesian models

Hyperparameter Tuning with Python - by Louis Owen (Paperback)

Grokking Data Structures - by Marcello La Rocca (Paperback)

Introduction to Linear Regression Analysis - (Wiley Probability and Statistics) 6th Edition by Douglas C Montgomery (Hardcover)

ML and Generative AI in the Data Lakehouse - by Bennie Haelen (Paperback)

Data-Centric Machine Learning with Python - by Jonas Christensen & Nakul Bajaj & Manmohan Gosada (Paperback)

Learn AI Data Engineering - by David Melillo (Paperback)

Basic Statistics for Life Scientists - by Jakub Tomek (Hardcover)

Modeling Decisions for Artificial Intelligence - by Vicenc Torra & Yasuo Narukawa (Paperback)

LLM Evaluation and Alignment, the Foundational Ideas - by Han Lee (Paperback)

A First Course in Bayesian Statistical Methods - (Springer Texts in Statistics) by Peter D Hoff (Hardcover)

Machine Learning - (Springer Reliability Engineering) by Xufeng Zhao & Qihui Wu & Hoang Pham (Hardcover)

Hands-On Large Language Models - by Jay Alammar & Maarten Grootendorst (Paperback)

GMAT Official Guide Data Insights Review 2026 - 2027 - (Paperback)

Predictive Modeling with SAS Enterprise Miner - 3rd Edition by Kattamuri S Sarma (Paperback)

Prediction Machines, Updated and Expanded - by Ajay Agrawal & Joshua Gans & Avi Goldfarb (Hardcover)

Fundamentals of Deep Learning - 2nd Edition by Nithin Buduma & Nikhil Buduma & Joe Papa (Paperback)

Neural Networks and Deep Learning - 2nd Edition by Charu C Aggarwal (Hardcover)

Polars Cookbook - by Yuki Kakegawa (Paperback)

Data Science First - by John Hawkins (Paperback)

Grokking Deep Learning - by Andrew Trask (Paperback)

Optimization and Learning Via Stochastic Gradient Search - (Princeton Applied Mathematics) by Felisa Vázquez-Abad & Bernd Heidergott (Hardcover)

Generative Ai: The Insights You Need from Harvard Business Review - (HBR Insights) (Paperback)

Machine Learning Design Patterns - by Valliappa Lakshmanan & Sara Robinson & Michael Munn (Paperback)