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The Mathematics of Large Language Models. Understand transformers, deep learning, and generative AI through equations and derivations Jason J. Karpeles MA

Książka w języku angielskim angielski
The Mathematics of Large Language Models. Understand transformers, deep learning, and generative AI through equations and derivations Jason J. Karpeles MA - okladka książki

The Mathematics of Large Language Models. Understand transformers, deep learning, and generative AI through equations and derivations Jason J. Karpeles MA - okladka książki

The Mathematics of Large Language Models. Understand transformers, deep learning, and generative AI through equations and derivations Jason J. Karpeles MA - audiobook MP3

The Mathematics of Large Language Models. Understand transformers, deep learning, and generative AI through equations and derivations Jason J. Karpeles MA - audiobook CD

Autor:
Jason J. Karpeles MA
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584
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Modern AI spans more than neural-network architectures: understanding why models learn, generalize, generate, scale, and fail requires the mathematics connecting them. This book builds that foundation systematically, starting with notation, approximation and expressivity, optimization and training dynamics, generalization, symmetry, and geometric inductive bias.

Written by Jason Karpeles, a former world No. 5-ranked Kaggle competitor with 30 years of predictive-modeling experience, the book keeps important equations and derivations intact. For each key formula, it explains both what the equation does and how to read its notation symbol by symbol. You’ll then examine convolutional and multiscale models, recurrent and state-space models, attention and transformers, graph and geometric deep learning, and generative approaches spanning latent-variable, flow, diffusion, and score-based models.

Later chapters cover neural ODEs, scientific machine learning, Bayesian deep learning, uncertainty, robustness, out-of-distribution behavior, causality, scaling laws, and in-context learning. The final chapter examines truth, error, abstention, and hallucination as mathematical problems. By connecting theory with clear interpretation, the book helps you follow AI research, compare model families, and reason precisely about model behavior.

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