{"product_id":"deep-reinforcement-learning-with-python-rlhf-for-chatbots-and-large-language-models-paperback","title":"Deep Reinforcement Learning with Python: Rlhf for Chatbots and Large Language Models - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eNimish Sanghi\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eGain a theoretical understanding to 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. \u003c\/p\u003e\u003cp\u003eNew 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 covers how multiple agents compete, 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 by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.\u003c\/p\u003e\u003cp\u003eYou'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 canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. \u003c\/p\u003e\u003cp\u003eWhether it's for applications in gaming, robotics, or Generative AI, \u003ci\u003eDeep Reinforcement Learning with Python\u003c\/i\u003e will help keep you ahead of the curve.\u003c\/p\u003e\u003cbr\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore Python-based RL libraries, including StableBaselines3 and CleanRL \u003c\/li\u003e\n\u003cli\u003eWork with diverse RL environments like Gymnasium, Pybullet, and Unity ML\u003c\/li\u003e\n\u003cli\u003eUnderstand instruction finetuning of Large Language Models using RLHF and PPO\u003c\/li\u003e\n\u003cli\u003eStudy training and optimization techniques using HuggingFace, Weights and Biases, and Optuna \u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e \u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch. \u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eGain 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.\u003c\/p\u003e \u003cp\u003eNew 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.\u003c\/p\u003e \u003cp\u003eYou'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.\u003c\/p\u003e \u003cp\u003eWhether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eNimish is a seasoned entrepreneur and an angel investor, with a rich portfolio of tech ventures in SaaS Software and Automation with AI across India, the US and Singapore. He has over 30 years of work experience. Nimish ventured into entrepreneurship in 2006 after holding leadership roles at global corporations like PwC, IBM, and Oracle.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eNimish holds an MBA from Indian Institute of Management, Ahmedabad, India (IIMA), and a Bachelor of Technology in Electrical Engineering from Indian Institute of Technology, Kanpur, India (IITK). ​\u003c\/p\u003e\u003cbr\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 634\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.33 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 15, 2024\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43153278271551,"sku":"9798868802720","price":64.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/1lJYvswmTd9798868802720.webp?v=1776945822","url":"https:\/\/dhl-adrianne.myshopify.com\/products\/deep-reinforcement-learning-with-python-rlhf-for-chatbots-and-large-language-models-paperback","provider":"BBB","version":"1.0","type":"link"}