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Generative AI .

生成式人工智能:端到端開發與 LLMs

51 hours

為什麼生成式人工智能對公司發展很重要,但很多企業對ChatGPT使用存在疑慮?

為什麼我們需要學習建立個人化的虛擬助手,而不能單單使用Bard、ChatGPT來推動企業創新?

其實生成式人工智能不止是ChatGPT!

了解生成式人工智慧(GAI)的運作方式

基於最新的GAI發展評估商業應用案例

針對股票LLM聊天機器人進行性能調優

編寫個人化LLM驅動聊天機器人

使用您的個人風格/選定領域配置和部署LLM驅動的聊天機器人

Generative AI: End-to-end development with LLMs

課程介紹:

這個課程旨在教授您如何快速利用大型語言模型(LLM)的強大功能。從了解LLMs的概念和運作方式,到將其應用和優化,透過提示工程、模型管控、前端開發和性能調優等方面的學習,本課程將使您掌握開發選定領域的尖端LLM解決方案所需的技能和知識。

Course Contents


• 了解生成式人工智慧(GAI)的運作方式
• 基於最新的GAI發展評估商業應用案例
• 針對股票LLM聊天機器人進行性能調優
• 編寫個人化LLM驅動聊天機器人
• 使用您的個人風格/選定領域配置和部署LLM驅動的聊天機器人

Course Trainer

Trainer

Henry Fong

Experience

• 全球技術咨詢公司中負責全球銀行和保險業的生成式人工智能項目經理
• 擁有數據科學、全棧開發、雲工程等方面的強大技• 術背景 擁有數據分析碩士學位和軟體工程學士學位
• AWS和GCP認證持有者

Still have questions? WhatsApp Us here or email at course@venturenixlab.com

ABOUT
Who should Enroll?
Web Designers 📝
Graphic Designers
Marketing Professionals
Business Analysts
Software Developers
Data Analysts
UI Designers
Project Managers

Still have questions? WhatsApp Us here or email at course@venturenixlab.com

Curriculum

Course Contents ⚡

The course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for selected domain.

WEEK 1 Introduction to Python: 1/3

Understand the building blocks of virtually all programming languages.

Content: Variables, input/output, data types, control structure, scope, function, error handling

WEEK 2 Introduction to Python 2/3

More on data type and control structure. introduction to object oriented programming

Content: Dictionaries, iteratives, OOP,  Pydantic, Modules in Python

WEEK 3 Introduction to Python 3/3

Introduction to Microservices and Environment Setup

Content: Restful API, Flask, FastAPI, Conda, venv, pip, Jupyter notebook

WEEK 4 Understanding Neuron Networks: 1/3

Understanding what neuron network is and how to build it with PyTorch

Content: Structure of neuron network. Why does it matter? Build your first NN using PyTorch

WEEK 5 Understanding Neuron Networks: 2/3

More on PyTorch. Why is GAN important?

Content: More hands-on development with Pytorch (e.g. CNN). Introduction to GAN.

WEEK 6 Understanding Neuron Networks: 3/3

Have fun on building your own GPT model

Content: Build your own GPT model with PyTorch (nanoGPT)

WEEK 7 Introduction to Cloud: 1/3

Introduction to most common cloud services. Introduction to docker.

Content: Compute, block storage, serverless, VPCs. Docker, Jupyter in docker, build your first docker image.

WEEK 8 Introduction to Cloud: 2/3

Introduction to kubernetes. Running your first Kubernetes Cluster and introduction to Terraform.

Content: Introduction to Kubernetes. Running your first cluster (with GPU). Replicate the cluster with Terraform script.

WEEK 9 Introduction to LLM: 1/3

Play around with LLMs on the market and understand the difference. Deploy your first LLM.

Content: Understanding LLMs configurations and parameters. Deploy a minimum LLM virtual assistant.

WEEK 10 Introduction to LLM: 2/3

Natural Language Processing: Why LLM rocks (transformer). Introduction to Prompt Engineering.

Content: Introduction to legacy NLP. What is transformer. Basic prompt engineering technical and concepts.

WEEK 11 Introduction to LLM: 3/3

Common LLM problems. How to improve LLM’s performance: PEFT and RAG

Content: Introduction to common LLM issues such as hallucination. Model fine-tuning vs RAG.

WEEK 12 LLM Orchestration: 1/3

What is LLM Orchestration and why do we need it. Introduction to LangChain.

Content: Introduction to LangChain concepts.

WEEK 13 LLM Orchestration: 2/3

Building RAG using Langchain. What is embedding? What is vector database? What is similarity search?

Content: Hands-on session on building a RAG using Langchain and vector database. Address common embedding issues.

WEEK 14 LLM Orchestration: 3/3

Dealing with structured database with Langchain. How to write your pre/post processor? Connecting your LLM to GUI.

Content: Hands-on session on using Langchain and structured data. How to extend Langchain. Building a simple GUI for your virtual assistant (e.g. streamlit).

WEEK 15 Capstone Project: 1/4

Picking your problem statement. Defining deliverables and acceptance criteria. Prepare solution architecture, model selection

WEEK 16 Capstone Project: 2/4

Preparing data, decide how to improve the performance (fine-tuning vs prompt engineering). Data pipeline design and implementation.

WEEK 17 Capstone Project: 3/4

Baseline your LLM’s performance, improve prompt template. Resolve known issues.

WEEK 18 Capstone Project: 4/4

Prepare terraform for the solution. Deploying to Cloud. Troubleshooting.

Curriculum
Course Contents

The course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for selected domain.

WEEK 1 Introduction to Python: 1/3

Understand the building blocks of virtually all programming languages.

Content: Variables, input/output, data types, control structure, scope, function, error handling

WEEK 2 Introduction to Python 2/3

More on data type and control structure. introduction to object oriented programming

Content: Dictionaries, iteratives, OOP,  Pydantic, Modules in Python

WEEK 3 Introduction to Python 3/3

Introduction to Microservices and Environment Setup

Content: Restful API, Flask, FastAPI, Conda, venv, pip, Jupyter notebook

WEEK 4 Understanding Neuron Networks: 1/3

Understanding what neuron network is and how to build it with PyTorch

Content: Structure of neuron network. Why does it matter? Build your first NN using PyTorch

WEEK 5 Understanding Neuron Networks: 2/3

More on PyTorch. Why is GAN important?

Content: More hands-on development with Pytorch (e.g. CNN). Introduction to GAN.

WEEK 6 Understanding Neuron Networks: 3/3

Have fun on building your own GPT model

Content: Build your own GPT model with PyTorch (nanoGPT)

WEEK 7 Introduction to Cloud: 1/3

Introduction to most common cloud services. Introduction to docker.

Content: Compute, block storage, serverless, VPCs. Docker, Jupyter in docker, build your first docker image.

WEEK 8 Introduction to Cloud: 2/3

Introduction to kubernetes. Running your first Kubernetes Cluster and introduction to Terraform.

Content: Introduction to Kubernetes. Running your first cluster (with GPU). Replicate the cluster with Terraform script.

WEEK 9 Introduction to LLM: 1/3

Play around with LLMs on the market and understand the difference. Deploy your first LLM.

Content: Understanding LLMs configurations and parameters. Deploy a minimum LLM virtual assistant.

WEEK 10 Introduction to LLM: 2/3

Natural Language Processing: Why LLM rocks (transformer). Introduction to Prompt Engineering.

Content: Introduction to legacy NLP. What is transformer. Basic prompt engineering technical and concepts.

WEEK 11 Introduction to LLM: 3/3

Common LLM problems. How to improve LLM’s performance: PEFT and RAG

Content: Introduction to common LLM issues such as hallucination. Model fine-tuning vs RAG.

WEEK 12 LLM Orchestration: 1/3

What is LLM Orchestration and why do we need it. Introduction to LangChain.

Content: Introduction to LangChain concepts.

WEEK 13 LLM Orchestration: 2/3

Building RAG using Langchain. What is embedding? What is vector database? What is similarity search?

Content: Hands-on session on building a RAG using Langchain and vector database. Address common embedding issues.

WEEK 14 LLM Orchestration: 3/3

Dealing with structured database with Langchain. How to write your pre/post processor? Connecting your LLM to GUI.

Content: Hands-on session on using Langchain and structured data. How to extend Langchain. Building a simple GUI for your virtual assistant (e.g. streamlit).

WEEK 15 Capstone Project: 1/4

Picking your problem statement. Defining deliverables and acceptance criteria. Prepare solution architecture, model selection

WEEK 16 Capstone Project: 2/4

Preparing data, decide how to improve the performance (fine-tuning vs prompt engineering). Data pipeline design and implementation.

WEEK 17 Capstone Project: 3/4

Baseline your LLM’s performance, improve prompt template. Resolve known issues.

WEEK 18 Capstone Project: 4/4

Prepare terraform for the solution. Deploying to Cloud. Troubleshooting.

Still have questions? WhatsApp Us here or email at course@venturenixlab.com

FAQ

Frequently Asked Questions

Students should ideally have general IT knowledge + knowledge of how programming works.
Knowing the difference of front-end and back-end, basic understand on how loop, for loop works in Python is a big plus.
Generative AI is not so common in Hong Kong now. But we can see there will be a high chance to have an AI team/ workplace automation team in large companies in near future. You can practice the skills in the company and acquire some related job experience. There are some jobs that require LLM too.
 
If you believe Generative AI is going to transform how future workplace and business works, this course is the one, if not the only one that teaches you hands-on how to develop an open source Generative AI application. (i.e. Can be with Chatgpt, or with other LLM model in case your business cannot use Chatgpt).
 
This course aims to train pioneer of Generative AI talent in Hong Kong.

 

AI engineer, AI specialist, Business Analyst, Innovation specialist etc.
However, According to our observation in the market from insights provided by Venturenix Limited, our sister company and a leading IT recruitment firm in Hong Kong, there is an increasing demands for Generative AI and Python knowledge from business team.
 
It means accounting team, procurement team, HR team, Sales team, marketing team, are all looking for someone with their domain knowledge, to also know Generative AI to facilitate innovation within the team.
This will be the trend moving forward.
 
After studying this course, you may completely switch field to IT with the positions mentioned above, or you may seek to stay in your current industry but look for a new role that encourages innovation within your team.

If you aspire to become a business analyst, studying a course in generative AI is highly beneficial as it equips you with cutting-edge data analysis tools and techniques essential for identifying and solving complex business problems. This knowledge enhances your ability to improve performance, lower operation costs, increase project ROI, and make effective decisions. Furthermore, understanding generative AI fosters better team collaboration and risk management, crucial skills for any business analyst aiming to drive success and growth in an organization.

The skill of “Can build a generative AI use case for own business/ company usage” will be very rare and hot in the market in the near future.
If they know the business insight and can develop an AI tools into business use, will be very valuable
“Innovate within the department” will come soon

BA that don’t know coding will start to lose their value.

Outside course is not able to pass the technical interview.
We teach them to build a project in their own-> this skill and knowledge is the most valuable

Project全世界都做同一個?

After completing a course in generative AI, students typically come away with tangible projects or applications they've developed, such as a custom-designed chatbot or a unique AI-driven project. These creations showcase their practical skills in utilizing AI for real-world applications, demonstrating their ability to design, implement, and manage AI systems. Such projects serve as a portfolio piece, evidencing their proficiency in AI technologies and their application in business analysis, which can be valuable for career advancement or academic pursuits.

Still have a question? WhatsApp Us here or email at course@venturenixlab.com

FAQ
Frequently Asked Questions

Students should ideally have general IT knowledge + knowledge of how programming works.
Knowing the difference of front-end and back-end is a plus.

IT is not so common in Hong Kong now. But we can see there will be a high chance to have an AI team/ workplace automation team in large companies in near future. You can practice the skills in the company and acquire some related job experience. There are some jobs that require LLM too.

Business Analyst roles have arisen as the top choice for individuals that have knowledge of LLMs and generative AI. 

Yes, you get access to all the demos and templates with a single license. The system includes a wide range of pre-built templates and demos that you can use to quickly create your own custom designs. These templates are fully customizable and can be easily modified to suit your specific needs. With a single license, you can use these templates to create unlimited designs without any additional fees or restrictions.

The skill of “Can build a generative AI use case for own business/ company usage” will be very rare and hot in the market in the near future.
If they know the business insight and can develop an AI tools into business use, will be very valuable
“Innovate within the department” will come soon

BA that don’t know coding will start to lose their value.

Outside course is not able to pass the technical interview.
We teach them to build a project in their own-> this skill and knowledge is the most valuable

Project全世界都做同一個?

Still have questions? WhatsApp Us here or email at course@venturenixlab.com