1 Training Overview
With the emergence of large language models like ChatGPT/GPT-4 and AI drawing tools such as Midjourney and Stable Diffusion, we have entered the AI 2.0 era in 2023. Microsoft founder Bill Gates has stated that the advent of ChatGPT has significant historical implications, comparable to the advent of the Internet and personal computers. Zhou Hongyi, founder of 360, believes that industries that fail to embrace this trend may face elimination. In the AI 2.0 era, AI thinking has become an essential skill. Therefore, we need to understand and master AI thinking to better adapt to the developmental trends of this era. The best way to master AI thinking is through practical learning. Our course will focus on practical operations, guiding you to deeply experience the wonders of ChatGPT and explore the infinite possibilities of AI drawing. The course covers the fundamentals, practical skills, and real-world cases of ChatGPT and AI drawing, aiming to leverage the latest AI tools to enhance your productivity and creativity tenfold.
2 Training Highlights
1. One month of free ChatGPT-4 membership account will be provided, along with a lifetime independent ChatGPT account usable on the OpenAI official website. We will also provide access to ChatGPT servers that can be scientifically accessed domestically, ensuring that everyone can practice the content learned in the course.
2. Each participant will receive access to domestic AI drawing tools and methods for local deployment, with practical learning in AI drawing during the course.
3. This training offers permanent Q&A services. You can always reach out to the instructor for communication during the practical learning process after class.
4. After participating in this training, you can attend the same training online once for free in the future, and join on-site training for free for life, without limits on the number of times.
5. The first 30 registrants will receive past training videos and materials for free.
6. A complete video tutorial set will be provided after the training.
3 Time and Training Format
May 24, 2024 — May 26, 2024
On-site in Guangzhou with live broadcast via Tencent Meeting for 3 days
June 21, 2024 — June 23, 2024
On-site in Beijing with live broadcast via Tencent Meeting for 3 days
Note: Two sessions of the course will be conducted simultaneously on-site and online. Please choose flexibly according to your situation.
4 Expert Speakers
This training invites senior experts from institutions such as the Chinese Academy of Sciences and Tsinghua University, with 10 years of experience in AI project development and 8 years in AI industry training. They have published multiple books on artificial intelligence and have led over 10 major national and enterprise projects, holding over 10 invention patents. They have previously completed multiple AI projects related to image processing, NLP, speech, and search for companies such as Shanghai General Motors, Shanghai Meteorological Bureau, various universities, research institutes, and companies.
They have conducted internal training and project collaborations on artificial intelligence for over a hundred enterprises and universities, including China Mobile, China Unicom, China Telecom, Bank of China, Huaxia Bank, Pacific Insurance, State Grid, China Railway Institute, CNOOC, Gree Electric Appliances, and Panasonic.
5 Training Outline
|
Main Chapter |
Sub Chapter |
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Chapter 1: Introduction to the Latest Developments in AI in 2024 |
1. (Practical Exercise) Explanation of the latest powerful model Claude3 2. Introduction to OpenAI’s new model – GPT-5 3. (Practical Exercise) Explanation of Google’s new model – Gemini 4. Introduction to Meta’s new model – LLama3 5. (Practical Exercise) Alibaba’s Tongyi Qianwen 6. (Practical Exercise) iFLYTEK’s Xinghuo Cognition 7. (Practical Exercise) Baidu’s Wenxin Yiyan 8. (Practical Exercise) MoonshotAI’s Kimi 9. (Practical Exercise) Zhizhu AI’s Zhizhu Qingyan 10. Detailed introduction to the latest model GPT-4 Turbo 11. Introduction to the latest released advanced data analysis, AI drawing, image recognition, and document API 12. Introduction to GPT Store 13. (Practical Exercise) Creating your own GPT application from scratch |
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Chapter 2: Detailed Explanation of Google’s Latest Model Gemini |
1. The three major models of Gemini 2. Comparison of Gemini and GPT-4 3. Native multimodal technology of Gemini 4. Testing results of Gemini 5. (Practical Exercise) Using Gemini |
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Chapter 3: Explanation of the Latest Powerful Model Claude3 |
1. The three major models of Claude3 2. Introduction to the Claude3 model team 3. Introduction to the technical details of Claude3 4. Comparison of Claude3 and GPT4 5. (Practical Exercise) Using Claude3 |
| Chapter 4: Overview of AIGC |
1. Overview of the AIGC course 2. Integration of AI tools and research applications 3. Introduction to the OpenAI Developer Conference 4. Development of AIGC technology 5. Introduction to GPT3.5/GPT4/GPT4-turbo models 6. Introduction to the concept of Tokens 7. Introduction to the context correlation of large language models 8. (In-class practical exercise) Using ChatGPT/GPT4 on the official website 9. (In-class practical exercise) Using ChatGPT/GPT4 domestically 10. (In-class practical exercise) Using the API of ChatGPT/GPT4 11. Introduction to prompt engineering 12. How to write effective prompts for a paper 13. How to communicate research questions with AI |
| Chapter 5: Introduction to AI Algorithms |
1. How AI algorithms are trained 2. Introduction to commonly used deep learning algorithms 3. Introduction to GPT1-3 models 4. Introduction to reinforcement learning and InstructGPT models 5. Introduction to RLHF human feedback reinforcement learning 6. Introduction to ChatGPT and GPT4 models |
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Chapter 6: Advanced Prompt Techniques for Large Language Models (LLM) |
1. Differences between large language models and search engines 2. Introduction to Prompt Engineering 3. (In-class practical exercise) Technique 1: Role-playing 4. (In-class practical exercise) Technique 2: Using different tones 5. (In-class practical exercise) Technique 3: Providing specific tasks 6. (In-class practical exercise) Technique 4: Utilizing context correlation features 7. (In-class practical exercise) Technique 5: Zero-shot reasoning prompts – enhancing model logical reasoning abilities 8. (In-class practical exercise) Technique 6: Few-shot reasoning prompts – enhancing model imitation abilities 9. (In-class practical exercise) Technique 7: Coherence – enhancing model mathematical capabilities 10. (In-class practical exercise) Technique 8: Generating knowledge prompts – enhancing model knowledge level |
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Chapter 7: Making GPT Your Personal Assistant |
1. (In-class practical exercise) Using GPT as a new search engine 2. (In-class practical exercise) GPT as an excellent translation tool 3. (In-class practical exercise) Letting GPT plan your travel itinerary 4. (In-class practical exercise) Making GPT your personal fitness coach 5. (In-class practical exercise) Making GPT your personal doctor 6. (In-class practical exercise) Making GPT teach you how to cook 7. (In-class practical exercise) Using GPT to guide your child’s learning 8. (In-class practical exercise) Using GPT to generate fairy tales 9. (In-class practical exercise) Using GPT to learn English 10. (In-class practical exercise) Using GPT for Socratic teaching 11. (In-class practical exercise) Using GPT to generate tabular data |
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Chapter 8: Making GPT Your Work Secretary |
1. (In-class practical exercise) Using GPT to organize article data 2. (In-class practical exercise) Using GPT for data processing 3. (In-class practical exercise) Using GPT to classify user comments 4. (In-class practical exercise) Using GPT to optimize work summaries 5. (In-class practical exercise) Using GPT to improve your products or services 6. (In-class practical exercise) Using GPT to analyze differences between different products 7. (In-class practical exercise) Seeking business and marketing advice from GPT 8. (In-class practical exercise) Using GPT to generate test questions for specific knowledge 9. (In-class practical exercise) Using GPT to write contracts 10. (In-class practical exercise) Using GPT to write resumes 11. (In-class practical exercise) Using GPT for mock interviews 12. (In-class practical exercise) Using GPT to generate and save mathematical formulas 13. (In-class practical exercise) Using GPT to generate charts based on specific data |
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Chapter 9: Making GPT Your Paper/Fund Assistant |
1. (In-class practical exercise) Introduction to paper search platforms 2. (In-class practical exercise) Expanding related papers based on a core paper 3. (In-class practical exercise) Determining whether an article is AI-generated 4. (In-class practical exercise) Uploading local PDF papers and letting GPT provide review comments 5. (In-class practical exercise) Uploading local PDF papers and letting GPT assist with translations 6. (In-class practical exercise) Uploading local PDF papers and letting GPT address related questions in the papers 7. (In-class practical exercise) Using GPT to generate paper abstracts 8. (In-class practical exercise) Using GPT to generate literature reviews 9. (In-class practical exercise) Using GPT for the technical methods in your paper 10. (In-class practical exercise) Using GPT for polishing Chinese papers 11. (In-class practical exercise) Using GPT for polishing bilingual papers 12. (In-class practical exercise) Using GPT to provide suggestions for paper revisions 13. (In-class practical exercise) Using GPT for translation and polishing 14. (In-class practical exercise) Using GPT for reducing paper similarity 15. (In-class practical exercise) Letting AI help you write a literature review and cite sources 16. (In-class practical exercise) Letting AI help you find papers related to a certain viewpoint or content 17. (In-class practical exercise) Letting AI help you find papers related to a certain paper 18. (In-class practical exercise) Using GPT to write a complete paper 19. (In-class practical exercise) Using GPT to polish an entire paper 20. (In-class practical exercise) Using GPT for paper searches 21. (In-class practical exercise) How to avoid detection of articles written by GPT |
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Chapter 10: Making GPT Your Programming Assistant |
1. (In-class practical exercise) Using GPT to implement a program with a specific function 2. (In-class practical exercise) Using GPT to explain code 3. (In-class practical exercise) Using GPT for code debugging and modification 4. (In-class practical exercise) Using GPT to answer coding questions 5. (In-class practical exercise) Using GPT to optimize code 6. (In-class practical exercise) Using GPT to read local data and write code 7. (In-class practical exercise) Letting GPT provide complete project code and continuously revise it 8. (In-class practical exercise) Introduction to automated AI programming assistants |
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Chapter 11: Machine Learning/Deep Learning Project Cases Based on GPT |
1. (In-class practical exercise) Using GPT to understand research/project-related knowledge 2. (In-class practical exercise) Using GPT to optimize research/project design 3. (In-class practical exercise) Using GPT to answer research/project-related questions 4. (In-class practical exercise) Using GPT to read local data (Excel or CSV data) 5. (In-class practical exercise) Using GPT to write deep learning modeling programs for research/project data 6. (In-class practical exercise) How to analyze feature importance (which features have the greatest impact on labels) 7. (In-class practical exercise) Comparing results of various commonly used machine learning algorithms |
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Chapter 12: Extended Applications of GPT |
1. (In-class practical exercise) Using AI tools to automatically create PPTs 2. (In-class practical exercise) Using AI tools to create PPTs based on article content 3. (In-class practical exercise) Using AI tools to quickly produce short videos 4. (In-class practical exercise) Using AI tools to quickly create flowcharts 5. (In-class practical exercise) Using AI tools to quickly create sequence diagrams 6. (In-class practical exercise) Using AI tools to quickly create mind maps 7. How large language models understand textual information 8. How large language models understand mathematics |
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Chapter 13: Learning ChatGPT/GPT-4 API Python Programming |
1. (In-class practical exercise) Basics of ChatGPT/GPT-4 API programming 2. (In-class practical exercise) Using API methods for article content inference 3. (In-class practical exercise) Introduction to parameters of ChatGPT/GPT-4 API 4. (In-class practical exercise) Creating a chatbot using ChatGPT/GPT-4 API 5. (In-class practical exercise) Creating a food ordering bot using ChatGPT/GPT-4 API 6. (In-class practical exercise) Batch processing of article content using ChatGPT/GPT-4 API 7. (In-class practical exercise) Generating images using DALL-E-3 API |
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Chapter 14: Detailed Explanation of ChatGPTPlus/GPT-4 Functions |
1. (In-class practical exercise) Using GPT-4 model 2. (In-class practical exercise) GPT-4 networking functions 2. (In-class practical exercise) GPT-4 recognizing product prices in images 3. (In-class practical exercise) GPT-4 recognizing liquid types in images 4. (In-class practical exercise) GPT-4 recognizing and solving math problems in images 5. (In-class practical exercise) GPT-4 recognizing landmarks in images 6. (In-class practical exercise) GPT-4 recognizing dishes in images 7. (In-class practical exercise) GPT-4 medical imaging diagnosis 8. (In-class practical exercise) GPT-4 recognizing statistical analysis graphs and generating corresponding plotting code 9. (In-class practical exercise) GPT-4 recognizing tables in images and saving them 10. (In-class practical exercise) GPT-4 recognizing and editing formulas in images 11. (In-class practical exercise) Explanation of formulas in GPT-4 papers |
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Chapter 15: Advanced Data Analysis with ChatGPTPlus/GPT-4 |
1. (In-class practical exercise) GPT-4’s ability to automatically write and run code 2. (In-class practical exercise) Using advanced data analysis functions for mathematical calculations 3. (In-class practical exercise) Using advanced data analysis functions to generate QR codes 4. (In-class practical exercise) Using advanced data analysis functions for image processing 5. (In-class practical exercise) Using advanced data analysis functions for text recognition 6. (In-class practical exercise) Data statistical analysis of student stress and mental health 7. (In-class practical exercise) Using advanced data analysis functions for automated data processing and analysis 8. (In-class practical exercise) Using GPT plugins to create statistical analysis graphs 9. (In-class practical exercise) Using GPT plugins for paper searches 10. (In-class practical exercise) Using GPT plugins for writing papers |
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Chapter 16: Customizing Your Own GPT Applications |
1. (In-class practical exercise) Introduction to popular custom GPT applications 2. (In-class practical exercise) Creating your own GPTs through chat interactions 3. (In-class practical exercise) Creating your own GPTs through customization 4. (In-class practical exercise) Three distribution methods for GPTs 5. (In-class practical exercise) Introduction to the action functions of GPTs 6. (In-class practical exercise) Paper improvement expert (GTPs) 7. (In-class practical exercise) Paper search (GTPs) 8. (In-class practical exercise) Three methods of paper writing (GTPs) |
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Chapter 17: Applications of Drawing Tools DALL-E2 and Midjourney |
1. Introduction to the principles of AI drawing 2. Introduction to text-to-image and image-to-image 3. Introduction to CLIP models and diffusion models 4. Introduction to the drawing tool DALL-E2 5. Introduction to the Midjourney tool 6. Improving resolution and fine-tuning images with Midjourney 7. Setting up a private server for Midjourney 8. Reference prompts for Midjourney 9. (In-class practical exercise) Introduction to remix mode 10. (In-class practical exercise) Introduction to the blend command 11. (In-class practical exercise) Introduction to the describe command 12. (In-class practical exercise) Generating new images from images 13. (In-class practical exercise) Introduction to parameters and settings for Midjourney 14. (In-class practical exercise) Using ChatGPT to generate image prompts 15. (In-class practical exercise) Generating high-quality images by combining parameter settings 16. (In-class practical exercise) Introduction to scientific drawing with Midjourney |
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Chapter 18: Basic Applications of Drawing Tool StableDiffusion |
1. Introduction to the StableDiffusion tool 2. Introduction to different models of StableDiffusion 3. Introduction to StableDiffusion environment deployment 4. Introduction to commonly used prompts in StableDiffusion 5. Introduction to the StableDiffusion interface 6. (In-class practical exercise) Generating images from text 7. (In-class practical exercise) Generating images from images 8. (In-class practical exercise) Reverse engineering prompts from images 9. (In-class practical exercise) Grammar and weight of prompts 10. (In-class practical exercise) Imitating others’ high-quality images to create new images 11. (In-class practical exercise) Smart image enlargement algorithms 12. (In-class practical exercise) Transforming anime characters into real people |
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Chapter 19: Advanced Applications of Drawing Tool StableDiffusion |
1. Downloading and deploying Lora models 2. (In-class practical exercise) Using Lora models to create realistic character images 3. (In-class practical exercise) Using Lora models to create anime character images 4. (In-class practical exercise) Using Inpainting for localized image re-drawing 5. Introduction to the plugin system of StableDiffusion 6. Introduction to Controlnet plugins 7. Demonstration of different model effects in Controlnet 8. (In-class practical exercise) Using line drawings to generate renovation and architecture 9. (In-class practical exercise) Using line drawings to color images 10. (In-class practical exercise) Generating images of characters in specific poses |
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Chapter 20: Latest Applications of Drawing Tool DALL-E3 |
1. Introduction to the DALL-E3 model 2. (In-class practical exercise) Combining DALL-E3 with GPT-4 3. (In-class practical exercise) Using Chinese prompts with DALL-E3 4. (In-class practical exercise) Modifying images based on contextual content with DALL-E3 5. (In-class practical exercise) Generating specific text in images with DALL-E3 6. (In-class practical exercise) Continuously optimizing DALL-E3 drawing results |
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Chapter 21: Applications in the AI Video Field (Sora, etc.) |
1. Introduction and use of video generation tool Pika 2. Introduction and use of video generation tool Runway 3. Introduction to the latest video generation model Sora from OpenAI 4. Using the latest video generation model Sora 5. Introduction to Alibaba’s video generation model EMO |
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Supplementary Course |
1. Course summary and outlook on technological development. 2. Based on students’ areas of interest, explaining how ChatGPT can be applied in those fields. 3. Establishing a Q&A group for students (providing lifelong free Q&A and one-on-one assistance after class). 4. Providing textbooks on AIGC/GPT/AI drawing, gradually improving skills after class. |
6 Fee Standards
There are three categories of training fees, please choose flexibly according to your needs.
Category A: 3900 yuan/person (includes training fee, material fee, Category A certificate fee, invoice fee, etc.) Accommodation is self-catered.
Certificate: A senior “AIGC Application Engineer” completion certificate issued by Zhongke Ruanyan (Beijing) Science and Technology Center will be awarded.
Category B: 4800 yuan/person (includes training fee, material fee, Category B certificate fee, invoice fee, etc.) Accommodation is self-catered.
Certificate: A senior “Large Model Application Development Engineer” professional technical talent skill certificate issued by the Career Development Planning Committee of the China Intelligent Engineering Research Association will be awarded, which is included in the committee’s database and is nationally verifiable, serving as valid proof for promotion and rating.
Category C: 5800 yuan/person (includes conference fee, material fee, Category B + Category C certificate fee, invoice fee, etc.) Accommodation is self-catered.
Certificate: A senior “Artificial Intelligence Application Engineer” vocational technical certificate issued by the Ministry of Industry and Information Technology will be awarded upon passing the exam, which can serve as proof of professional technical personnel’s vocational ability assessment, as well as an important basis for employment, appointment, grading, and promotion of professional technical personnel, and is nationally recognized and verifiable on the official website.
Provide formal VAT invoices for easy reimbursement. If you need to issue a conference fee invoice, a conference notice can be provided.
7 Registration MethodPlease scan the QR code below to register online. After successful registration, we will send you a training notification and confirm by phone.
Discount Policy:
1. Students can enjoy a discount of 300 yuan with their student ID;
2. For group registrations of two or more people, each person can reduce 200 yuan;
3. For group registrations of three or more people, each person can reduce 300 yuan;
4. For group registrations of four or more people, each person can reduce 400 yuan;
5. For group registrations of five or more people, an additional free spot will be given;
6. The above discount policies cannot be enjoyed simultaneously, only one can be applied.
