國立虎尾科技大學 |

Deep reinforcement learning with Python = RLHF for chatbots and large language models /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep reinforcement learning with Python/ by Nimish Sanghi.
其他題名:	RLHF for chatbots and large language models /
作者:	Sanghi, Nimish.
出版者:	Berkeley, CA :Apress : : 2024.,
面頁冊數:	xxv, 634 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Python (Computer program language) -
電子資源:	https://doi.org/10.1007/979-8-8688-0273-7
ISBN:	9798868802737

Deep reinforcement learning with Python = RLHF for chatbots and large language models /
Sanghi, Nimish.

Deep reinforcement learning with PythonRLHF for chatbots and large language models /[electronic resource] :by Nimish Sanghi. - Second edition. - Berkeley, CA :Apress :2024. - xxv, 634 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Reinforcement Learning -- Chapter 2: The Foundation - Markov Decision Processes -- Chapter 3: Model Based Approaches -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation and Deep Reinforcement Learning -- Chapter 6: Deep Q-Learning (DQN) -- Chapter 7: Improvements to DQN -- Chapter 8: Policy Gradient Algorithms -- Chapter 9: Combining Policy Gradient and Q-Learning -- Chapter 10: Integrated Planning and Learning -- Chapter 11: Proximal Policy Optimization (PPO) and RLHF -- Chapter 12: Introduction to Multi Agent RL (MARL) -- Chapter 13: Additional Topics and Recent Advances.

Gain a theoretical understanding of the most popular libraries in deep reinforcement learning (deep RL) This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning (MARL) covers how multiple agents can be trained, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO) You'll see how reinforcement learning with human feedback (RLHF) has been used to fine-tune Large Language Models (LLMs) to chat and follow instructions. An example of this is the OpenAI ChatGPT offering human like conversational capabilities. You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which can be run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.

ISBN: 9798868802737

Standard No.: 10.1007/979-8-8688-0273-7doiSubjects--Uniform Titles:

ChatGPT.
Subjects--Topical Terms:

566246
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Deep reinforcement learning with Python = RLHF for chatbots and large language models /
LDR:03004nam a2200337 a 4500 001 1134403
003 DE-He213
005 20240713130835.0
006 m d
007 cr nn 008maaau
008 241213s2024 cau s 0 eng d
020 $a 9798868802737 $q (electronic bk.)
020 $a 9798868802720 $q (paper)
024 7 $a 10.1007/979-8-8688-0273-7 $2 doi
035 $a 979-8-8688-0273-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b S225 2024
100 1 $a Sanghi, Nimish. $e author. $3 1354079
245 1 0 $a Deep reinforcement learning with Python $h [electronic resource] : $b RLHF for chatbots and large language models / $c by Nimish Sanghi.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xxv, 634 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to Reinforcement Learning -- Chapter 2: The Foundation - Markov Decision Processes -- Chapter 3: Model Based Approaches -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation and Deep Reinforcement Learning -- Chapter 6: Deep Q-Learning (DQN) -- Chapter 7: Improvements to DQN -- Chapter 8: Policy Gradient Algorithms -- Chapter 9: Combining Policy Gradient and Q-Learning -- Chapter 10: Integrated Planning and Learning -- Chapter 11: Proximal Policy Optimization (PPO) and RLHF -- Chapter 12: Introduction to Multi Agent RL (MARL) -- Chapter 13: Additional Topics and Recent Advances.
520 $a Gain a theoretical understanding of the most popular libraries in deep reinforcement learning (deep RL) This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning (MARL) covers how multiple agents can be trained, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO) You'll see how reinforcement learning with human feedback (RLHF) has been used to fine-tune Large Language Models (LLMs) to chat and follow instructions. An example of this is the OpenAI ChatGPT offering human like conversational capabilities. You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which can be run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.
630 0 0 $a ChatGPT. $3 1429760
650 0 $a Python (Computer program language) $3 566246
650 0 $a Natural language processing (Computer science) $3 641811
650 0 $a Artificial intelligence $x Computer programs. $3 896016
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Python. $3 1115944
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0273-7
950 $a Professional and Applied Computing (SpringerNature-12059)