Vedant vajre (6 resultados)

- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 83,53
Envío por EUR 2,67Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.

- Tapa blanda
Librería: moluna, Greven, Alemaniamoluna
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EUR 79,84
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Uday Kamath has over 25 years of experience in AI product development with a Ph.D. in scalable machine learning. His significant contributions span numerous journals, conferences, books, and patents. Notable books include Large Language Models: A Deep Di.

- Tapa blanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 108,05
Envío por EUR 35,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware - Reinforcement learning (RL) has become the engine behind some of the most significant advances in modern artificial intelligence, from defeating world champions in Go to aligning large language models with human preferences. Yet despite its central role, RL remains poorly understood by many practitioners who work with these systems daily. Reinforcement Learning in Action: From Foundations to Frontiers bridges the gap between classical RL theory and the cutting-edge techniques driving today's AI breakthroughs. The book traces a complete path from Markov Decision Processes and Bellman equations through deep RL methods (DQN, REINFORCE, Actor-Critic, PPO) to the modern landscape of LLM alignment (RLHF, DPO, SimPO, KTO), reasoning optimization (GRPO, VinePPO, MCTS), and agentic systems with tool use, memory, and multi-turn planning. A distinguishing feature is the book's consistent five-layer pedagogical structure: each algorithm is presented with its key characteristics, a full mathematical derivation, an honest assessment of its advantages and limitations, a complete from-scratch Python/PyTorch implementation in which variable names match the equations, and a hands-on case study with reproducible experiments. Case studies progress from Grid World navigation and CartPole control to fine-tuning language models with DPO on the HuggingFace ecosystem, training reasoning models with GRPO on mathematical benchmarks, and building a full agentic customer support system. Written for ML engineers, researchers, and advanced students, this book provides both the conceptual depth and implementation fluency needed to understand, build, and extend the RL systems shaping the future of AI.…

- Tapa dura
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 194,60
Envío por EUR 2,67Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.

- Tapa dura
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 179,94
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Uday Kamath has over 25 years of experience in AI product development with a Ph.D. in scalable machine learning. His significant contributions span numerous journals, conferences, books, and patents. Notable books include Large Language Models: A Deep Di.

- Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 240,59
Envío por EUR 35,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Buch. Condición: Neu. Neuware - Reinforcement learning (RL) has become the engine behind some of the most significant advances in modern artificial intelligence, from defeating world champions in Go to aligning large language models with human preferences. Yet despite its central role, RL remains poorly understood by many practitioners who work with these systems daily. Reinforcement Learning in Action: From Foundations to Frontiers bridges the gap between classical RL theory and the cutting-edge techniques driving today's AI breakthroughs. The book traces a complete path from Markov Decision Processes and Bellman equations through deep RL methods (DQN, REINFORCE, Actor-Critic, PPO) to the modern landscape of LLM alignment (RLHF, DPO, SimPO, KTO), reasoning optimization (GRPO, VinePPO, MCTS), and agentic systems with tool use, memory, and multi-turn planning. A distinguishing feature is the book's consistent five-layer pedagogical structure: each algorithm is presented with its key characteristics, a full mathematical derivation, an honest assessment of its advantages and limitations, a complete from-scratch Python/PyTorch implementation in which variable names match the equations, and a hands-on case study with reproducible experiments. Case studies progress from Grid World navigation and CartPole control to fine-tuning language models with DPO on the HuggingFace ecosystem, training reasoning models with GRPO on mathematical benchmarks, and building a full agentic customer support system. Written for ML engineers, researchers, and advanced students, this book provides both the conceptual depth and implementation fluency needed to understand, build, and extend the RL systems shaping the future of AI.…