Course_Events-1-Part_2_release_event
中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/course/events/2?fw=pt
Part 2 Release Event
第2部分发布活动
For the release of part 2 of the course, we organized a live event with two days of talks before a fine-tuning sprint. If you missed it, you can catch up with the talks which are all listed below!
为了发布课程的第二部分，我们组织了一个现场活动，在进行微调冲刺之前进行了为期两天的演讲。如果你错过了它，你可以赶上下面列出的讲座！
Day 1: A high-level view of Transformers and how to train them
第一天：对Transformer的高层次了解以及如何培训他们
Thomas Wolf: Transfer Learning and the birth of the Transformers library
托马斯·沃尔夫：迁移学习与Transformer图书馆的诞生
Thom演讲的直观总结
Thomas Wolf is co-founder and Chief Science Officer of Hugging Face. The tools created by Thomas Wolf and the Hugging Face team are used across more than 5,000 research organisations including Facebook Artificial Intelligence Research, Google Research, DeepMind, Amazon Research, Apple, the Allen Institute for Artificial Intelligence as well as most university departments. Thomas Wolf is the initiator and senior chair of the largest research collaboration that has ever existed in Artificial Intelligence: “BigScience”, as well as a set of widely used libraries and tools. Thomas Wolf is also a prolific educator, a thought leader in the field of Artificial Intelligence and Natural Language Processing, and a regular invited speaker to conferences all around the world https://thomwolf.io.
托马斯·沃尔夫是Hugging Face的联合创始人兼首席科学官。托马斯·沃尔夫和Hugging Face团队开发的这些工具被5000多个研究机构使用，包括Facebook人工智能研究、谷歌研究、DeepMind、亚马逊研究、苹果、艾伦人工智能研究所以及大多数大学院系。托马斯·沃尔夫是人工智能领域有史以来最大的研究合作–“BigScience”的发起人和高级主席，以及一系列广泛使用的库和工具。Thomas Wolf也是一位多产的教育家，人工智能和自然语言处理领域的思想领袖，并定期在世界各地的会议上特邀演讲。https://thomwolf.io.
Jay Alammar: A gentle visual intro to Transformers models
Jay Alammar：Transformer模型的温和视觉介绍
周讲话的直观总结
Through his popular ML blog, Jay has helped millions of researchers and engineers visually understand machine learning tools and concepts from the basic (ending up in NumPy, Pandas docs) to the cutting-edge (Transformers, BERT, GPT-3).
通过他广受欢迎的ML博客，Jay帮助数百万研究人员和工程师直观地理解了从基础(最终出现在NumPy，Pandas Docs)到尖端(Transformers，Bert，GPT-3)的机器学习工具和概念。
Margaret Mitchell: On Values in ML Development
玛格丽特·米切尔：关于ML发展中的价值观
玛格丽特演讲的直观总结
Margaret Mitchell is a researcher working on Ethical AI, currently focused on the ins and outs of ethics-informed AI development in tech. She has published over 50 papers on natural language generation, assistive technology, computer vision, and AI ethics, and holds multiple patents in the areas of conversation generation and sentiment classification. She previously worked at Google AI as a Staff Research Scientist, where she founded and co-led Google’s Ethical AI group, focused on foundational AI ethics research and operationalizing AI ethics Google-internally. Before joining Google, she was a researcher at Microsoft Research, focused on computer vision-to-language generation; and was a postdoc at Johns Hopkins, focused on Bayesian modeling and information extraction. She holds a PhD in Computer Science from the University of Aberdeen and a Master’s in computational linguistics from the University of Washington. While earning her degrees, she also worked from 2005-2012 on machine learning, neurological disorders, and assistive technology at Oregon Health and Science University. She has spearheaded a number of workshops and initiatives at the intersections of diversity, inclusion, computer science, and ethics. Her work has received awards from Secretary of Defense Ash Carter and the American Foundation for the Blind, and has been implemented by multiple technology companies. She likes gardening, dogs, and cats.
玛格丽特·米切尔是一名致力于伦理人工智能的研究人员，目前专注于科技领域基于伦理的人工智能发展的细节。她发表了50多篇关于自然语言生成、辅助技术、计算机视觉和人工智能伦理的论文，并在对话生成和情感分类领域拥有多项专利。她之前在谷歌人工智能担任员工研究科学家，在那里她创立并共同领导了谷歌的人工智能伦理小组，专注于基础人工智能伦理研究和在谷歌内部实施人工智能伦理。在加入谷歌之前，她是微软研究院的研究员，专注于计算机视觉到语言的生成；她是约翰·霍普金斯大学的博士后，专注于贝叶斯建模和信息提取。她拥有阿伯丁大学的计算机科学博士学位和华盛顿大学的计算语言学硕士学位。在获得学位的同时，她还在2005-2012年间在俄勒冈健康与科学大学从事机器学习、神经疾病和辅助技术方面的工作。她在多样性、包容性、计算机科学和伦理学的交叉点上领导了许多研讨会和倡议。她的工作获得了国防部长阿什·卡特和美国盲人基金会的奖励，并被多家科技公司实施。她喜欢园艺、狗和猫。
Matthew Watson and Chen Qian: NLP workflows with Keras
马修·沃森和陈潜：凯拉斯的自然语言编程工作流
马特和陈冠希谈话的直观总结
Matthew Watson is a machine learning engineer on the Keras team, with a focus on high-level modeling APIs. He studied Computer Graphics during undergrad and a Masters at Stanford University. An almost English major who turned towards computer science, he is passionate about working across disciplines and making NLP accessible to a wider audience.
Matthew Watson是Kera团队的机器学习工程师，专注于高级建模API。在斯坦福大学读本科和硕士期间，他学习了计算机图形学。作为一名几乎主修英语的学生，他转向了计算机科学，他热衷于跨学科工作，并让更广泛的受众能够接触到NLP。
Chen Qian is a software engineer from Keras team, with a focus on high-level modeling APIs. Chen got a Master degree of Electrical Engineering from Stanford University, and he is especially interested in simplifying code implementations of ML tasks and large-scale ML.
陈潜是KERAS团队的一名软件工程师，专注于高级建模API。Chen拥有斯坦福大学电气工程硕士学位，他对简化ML任务和大规模ML的代码实现特别感兴趣。
Mark Saroufim: How to Train a Model with Pytorch
Mark Saroufim：如何用火炬训练模特
马克演讲的直观总结
Mark Saroufim is a Partner Engineer at Pytorch working on OSS production tools including TorchServe and Pytorch Enterprise. In his past lives, Mark was an Applied Scientist and Product Manager at Graphcore, yuri.ai, Microsoft and NASA’s JPL. His primary passion is to make programming more fun.
Mark Saroufim是Pytorch的合伙人工程师，致力于开发包括TorchServe和Pytorch Enterprise在内的OSS生产工具。在他过去的生活中，马克是Graphcore、yuri.ai、微软和NASA喷气推进实验室的应用科学家和产品经理。他的主要爱好是让编程变得更有趣。
Jakob Uszkoreit: It Ain’t Broke So Don’t Fix Let’s Break It
Jakob Uszkoreit：它没有坏，所以不要修了，让我们把它弄坏
雅各布讲话的直观总结
Jakob Uszkoreit is the co-founder of Inceptive. Inceptive designs RNA molecules for vaccines and therapeutics using large-scale deep learning in a tight loop with high throughput experiments with the goal of making RNA-based medicines more accessible, more effective and more broadly applicable. Previously, Jakob worked at Google for more than a decade, leading research and development teams in Google Brain, Research and Search working on deep learning fundamentals, computer vision, language understanding and machine translation.
Jakob Uszkoreit是Inceptive的联合创始人。初始设计用于疫苗和治疗的RNA分子使用大规模的深度学习在一个紧密的循环中进行高通量实验，目的是使基于RNA的药物更容易获得、更有效和更广泛的应用。此前，雅各布在谷歌工作了十多年，领导谷歌大脑、研究和搜索领域的研发团队，致力于深度学习基础知识、计算机视觉、语言理解和机器翻译。
Day 2: The tools to use
第二天：要使用的工具
Lewis Tunstall: Simple Training with the 🤗 Transformers Trainer
刘易斯·汤斯托：🤗Transformer训练师的简单培训
Lewis is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O’Reilly book Natural Language Processing with Transformers. You can follow him on Twitter (@_lewtun) for NLP tips and tricks!
刘易斯是一名机器学习工程师，致力于开发开源工具，并让更广泛的社区能够访问它们。他也是O‘Reilly出版的《与Transformer的自然语言处理》一书的合著者。你可以在推特(@_lewtun)上关注他，获取NLP的提示和诀窍！
Matthew Carrigan: New TensorFlow Features for 🤗 Transformers and 🤗 Datasets
Matthew Carrigan：🤗Transformer和🤗数据集的新传感器流功能
Matt is responsible for TensorFlow maintenance at Transformers, and will eventually lead a coup against the incumbent PyTorch faction which will likely be co-ordinated via his Twitter account @carrigmat.
马特负责TensorFlow在Transformer的维护，他最终将领导一场针对现任PyTorch派别的政变，这场政变很可能会通过他的推特账户@carrigmat进行协调。
Lysandre Debut: The Hugging Face Hub as a means to collaborate on and share Machine Learning projects
LySandre首次亮相：Hugging Face中心作为合作和分享机器学习项目的手段
李珊卓演讲的视觉总结
Lysandre is a Machine Learning Engineer at Hugging Face where he is involved in many open source projects. His aim is to make Machine Learning accessible to everyone by developing powerful tools with a very simple API.
LySandre是Hugging Face的机器学习工程师，在那里他参与了许多开源项目。他的目标是通过开发功能强大的工具和非常简单的API，使每个人都可以访问机器学习。
Lucile Saulnier: Get your own tokenizer with 🤗 Transformers & 🤗 Tokenizers
Lucile Saulnier：使用🤗Transformers和🤗Tokenizers获得您自己的令牌器
Lucile is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience.
Lucile是一名机器学习工程师，擅长Hugging Face、开发和支持开源工具的使用。她还积极参与了自然语言处理领域的许多研究项目，如协作培训和BigScience。
Sylvain Gugger: Supercharge your PyTorch training loop with 🤗 Accelerate
西尔万·古格：用🤗Accelerate为你的火炬训练循环增压
Sylvain is a Research Engineer at Hugging Face and one of the core maintainers of 🤗 Transformers and the developer behind 🤗 Accelerate. He likes making model training more accessible.
Sylvain是Hugging Face的研究工程师，也是🤗Transformer的核心维护者之一，也是🤗Accelerate背后的开发者。他喜欢让模特培训变得更容易接受。
Merve Noyan: Showcase your model demos with 🤗 Spaces
梅尔维·诺扬：用🤗Spaces展示你的模特演示
Merve is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone.
Merve是一位拥护面孔的开发人员，他致力于开发工具并围绕这些工具构建内容，以使每个人的机器学习民主化。
Abubakar Abid: Building Machine Learning Applications Fast
阿布巴卡尔·阿比德：快速构建机器学习应用程序
阿布巴卡尔演讲的视觉总结
Abubakar Abid is the CEO of Gradio. He received his Bachelor’s of Science in Electrical Engineering and Computer Science from MIT in 2015, and his PhD in Applied Machine Learning from Stanford in 2021. In his role as the CEO of Gradio, Abubakar works on making machine learning models easier to demo, debug, and deploy.
阿布巴卡尔·阿比德是GRadio的首席执行官。他于2015年获得麻省理工学院电气工程和计算机科学学士学位，并于2021年获得斯坦福大学应用机器学习博士学位。作为GRadio的首席执行官，Abubakar致力于使机器学习模型更易于演示、调试和部署。
Mathieu Desvé: AWS ML Vision: Making Machine Learning Accessible to all Customers
Mathieu Desvé：AWS ML愿景：让所有客户都能访问机器学习
马修演讲的视觉总结
Technology enthusiast, maker on my free time. I like challenges and solving problem of clients and users, and work with talented people to learn every day. Since 2004, I work in multiple positions switching from frontend, backend, infrastructure, operations and managements. Try to solve commons technical and managerial issues in agile manner.
科技发烧友，我空闲时间的制造者。我喜欢挑战和解决客户和用户的问题，每天都和有才华的人一起学习。自2004年以来，我在多个职位上工作，从前端、后端、基础设施、运营和管理。尝试以敏捷的方式解决公共的技术和管理问题。
Philipp Schmid: Managed Training with Amazon SageMaker and 🤗 Transformers
菲利普·施密德：亚马逊SageMaker和🤗Transformer的管理培训
Philipp Schmid is a Machine Learning Engineer and Tech Lead at Hugging Face, where he leads the collaboration with the Amazon SageMaker team. He is passionate about democratizing and productionizing cutting-edge NLP models and improving the ease of use for Deep Learning.
菲利普·施密德是一名机器学习工程师，也是Hugging Face的技术主管，在那里他领导着与亚马逊SageMaker团队的合作。他热衷于将尖端的NLP模型民主化和产品化，并提高深度学习的易用性。