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Titlebook: Deep Reinforcement Learning with Python; RLHF for Chatbots an Nimish Sanghi Book 2024Latest edition Nimish Sanghi 2024 Artificial Intellige

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樓主: 帳簿
21#
發(fā)表于 2025-3-25 04:20:43 | 只看該作者
22#
發(fā)表于 2025-3-25 08:01:32 | 只看該作者
23#
發(fā)表于 2025-3-25 12:41:11 | 只看該作者
24#
發(fā)表于 2025-3-25 19:16:06 | 只看該作者
25#
發(fā)表于 2025-3-25 20:59:44 | 只看該作者
26#
發(fā)表于 2025-3-26 00:36:32 | 只看該作者
,Führung in der ?ffentlichen Verwaltung,that has a good theoretical foundation and then with a nonlinear approach with neural networks. This aspect of combining deep learning with reinforcement learning is the most exciting development and has moved reinforcement learning algorithms to scale.
27#
發(fā)表于 2025-3-26 07:22:34 | 只看該作者
ears is still the state-of-the-art policy-based optimization technique in RL. This is followed by a quick overview of LLMs—the architecture, the training process, and the overall LLM ecosystem. The chapter walks through a complete demo of RLHF tuning on a LLM using the state-of-the-art approaches.
28#
發(fā)表于 2025-3-26 12:09:50 | 只看該作者
29#
發(fā)表于 2025-3-26 13:01:49 | 只看該作者
Proximal Policy Optimization (PPO) and RLHF,ears is still the state-of-the-art policy-based optimization technique in RL. This is followed by a quick overview of LLMs—the architecture, the training process, and the overall LLM ecosystem. The chapter walks through a complete demo of RLHF tuning on a LLM using the state-of-the-art approaches.
30#
發(fā)表于 2025-3-26 20:50:53 | 只看該作者
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