bayesian models

Grokking Statistics - by Thomas Nield (Paperback)

Grokking Machine Learning, Second Edition - by Luis Serrano (Paperback)

Causal AI - by Robert Osazuwa Ness (Paperback)

Google Machine Learning and Generative AI for Solutions Architects - by Kieran Kavanagh (Paperback)

Machine Learning Algorithms in Depth - by Vadim Smolyakov (Paperback)

Domain-Specific Small Language Models - by Guglielmo Iozzia (Paperback)

Large Language Models: A Deep Dive - by Uday Kamath & Kevin Keenan & Garrett Somers & Sarah Sorenson (Hardcover)

AI Model Evaluation - by Leemay Nassery (Paperback)

Equilibrium Distributions of Branching Processes - (Mathematics and Its Applications) by A Liemant & K Matthes & A Wakolbinger (Hardcover)

The Regularization Cookbook - by Vincent Vandenbussche (Paperback)

Deep Learning for Time Series Cookbook - by Vitor Cerqueira & Luís Roque (Paperback)

Outlier Detection in Python - by Brett Kennedy (Paperback)

How Large Language Models Work - by Edward Raff & Drew Farris & Stella Biderman (Paperback)

Let the Evidence Speak - by Alan Jessop (Paperback)

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

Deep Learning with Pytorch, Second Edition - by Luca Antiga & Eli Stevens & Howard Huang & Thomas Viehmann (Paperback)

Applied Reinforcement Learning - by Hadi Aghazadeh (Paperback)

Deep Learning with Keras - by Antonio Gulli & Sujit Pal (Paperback)

Interpretable Machine Learning with Python - Second Edition - 2nd Edition by Serg Masís (Paperback)

Grokking Deep Learning - by Andrew Trask (Paperback)

Building Large Language Models from Scratch - by Dilyan Grigorov (Paperback)

The AI Pocketbook - by Emmanuel Maggiori (Paperback)

Bernoulli's Fallacy - by Aubrey Clayton

Scientific Data: A 50 Steps Guide Using Python - (De Gruyter Textbook) by Matthias Josef Hofmann (Paperback)