Structural data analysis based on multilayer networkstm1966
Introduction on data analysis based on multilayer networks (in Japanese). Some references of tools, datasets, conferences and Web sites are also mentioned.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
Structural data analysis based on multilayer networkstm1966
Introduction on data analysis based on multilayer networks (in Japanese). Some references of tools, datasets, conferences and Web sites are also mentioned.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
AI for Everyone: Demystifying Large Language Models (LLMs) Like ChatGPTCprime
We’ve only scratched the surface of realizing the full potential of ChatGPT and other Large Language Models (LLMs) in a strategic business context. Here’s another game-changer: Incorporating your unique enterprise data with LLMs to tailor a private model that learns, retains, and utilizes your business’s unique information. It can deliver contextualized value and efficiency to enhance processes and better achieve strategic outcomes.
In this webinar, we’ll explore how these custom models are revolutionizing the business landscape with the added context of invaluable proprietary business knowledge. Join us to learn the power and practical applications of secure, private LLMs and catch live demonstrations to tangibly enforce how to tackle significant business challenges such as Agile Adoption and Service Management.
Learning Objectives
- Introduction to AI and LLMs: Understand the basics of Artificial Intelligence (AI) and how widely available Large Language Models (LLMs) are a key advancement in this field.
- Practical Applications of LLMs: Learn how LLMs can enhance operational processes and contribute to business growth in real-world scenarios, and how they can be customized to meet specific business needs.
- Benefits of Customization: Discover the advantages of tailoring AI solutions like LLMs to understand and support unique business requirements.
- Relevance and Precision: Learn how LLMs adapt to specific business contexts, ensuring that interactions are accurate and aligned with organizational objectives.
博士論文の執筆した時に作った,チェックリストをスライドにまとめました.
This slide is only for Japanese speakers
他に参考になるページ
+修士論文の作り方( http://itolab.is.ocha.ac.jp/~itot/lecture/msthesis.html ) by 伊藤先生
+修論(D論)参考( http://d.hatena.ne.jp/rkmt/20101217/1292573279 ) by 暦本純一先生
BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
AI for Everyone: Demystifying Large Language Models (LLMs) Like ChatGPTCprime
We’ve only scratched the surface of realizing the full potential of ChatGPT and other Large Language Models (LLMs) in a strategic business context. Here’s another game-changer: Incorporating your unique enterprise data with LLMs to tailor a private model that learns, retains, and utilizes your business’s unique information. It can deliver contextualized value and efficiency to enhance processes and better achieve strategic outcomes.
In this webinar, we’ll explore how these custom models are revolutionizing the business landscape with the added context of invaluable proprietary business knowledge. Join us to learn the power and practical applications of secure, private LLMs and catch live demonstrations to tangibly enforce how to tackle significant business challenges such as Agile Adoption and Service Management.
Learning Objectives
- Introduction to AI and LLMs: Understand the basics of Artificial Intelligence (AI) and how widely available Large Language Models (LLMs) are a key advancement in this field.
- Practical Applications of LLMs: Learn how LLMs can enhance operational processes and contribute to business growth in real-world scenarios, and how they can be customized to meet specific business needs.
- Benefits of Customization: Discover the advantages of tailoring AI solutions like LLMs to understand and support unique business requirements.
- Relevance and Precision: Learn how LLMs adapt to specific business contexts, ensuring that interactions are accurate and aligned with organizational objectives.
博士論文の執筆した時に作った,チェックリストをスライドにまとめました.
This slide is only for Japanese speakers
他に参考になるページ
+修士論文の作り方( http://itolab.is.ocha.ac.jp/~itot/lecture/msthesis.html ) by 伊藤先生
+修論(D論)参考( http://d.hatena.ne.jp/rkmt/20101217/1292573279 ) by 暦本純一先生
BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
アメリカ国立科学財団(NSF)が行った調査「Science and Engineering Doctorates」によると、(略)博士号取得者の年収は、学術機関で6万ドル(約680万円)、民間企業で10万ドル(約1,130万円)、行政機関8万5,000ドル(約960万円)です。アメリカ全体の平均年収が5万6,000ドル(約630万円)程度であることを考えると、優遇されていることがわかりますよね。
アメリカ国立科学財団(NSF)が行った調査「Science and Engineering Doctorates」によると、(略)博士号取得者の年収は、学術機関で6万ドル(約680万円)、民間企業で10万ドル(約1,130万円)、行政機関8万5,000ドル(約960万円)です。アメリカ全体の平均年収が5万6,000ドル(約630万円)程度であることを考えると、優遇されていることがわかりますよね。
For Shor’s algorithm
https://quantumexperience.ng.bluemix.net/qx/tutorial?sectionId=full-user-guide&page=introduction
アメリカ国立科学財団(NSF)が行った調査「Science and Engineering Doctorates」によると、(略)博士号取得者の年収は、学術機関で6万ドル(約680万円)、民間企業で10万ドル(約1,130万円)、行政機関8万5,000ドル(約960万円)です。アメリカ全体の平均年収が5万6,000ドル(約630万円)程度であることを考えると、優遇されていることがわかりますよね。