Generative AI Roadmap 2024 | Learn Gen AI in 6 Months | Build Your LLM, ChatGPT, Gemini, DALL-E, __?
TLDRThe video script outlines a six-month roadmap for individuals aspiring to build a career in generative AI, focusing on progression from user to super user, developer, and ultimately, researcher. It emphasizes the importance of understanding prompt engineering, API interactions, and fine-tuning models for domain-specific tasks. The journey involves hands-on experience with popular AI tools, learning programming languages like Python, and staying updated with the latest research. The script also mentions the option of enrolling in a generative AI Pinnacle program for a comprehensive learning experience.
Takeaways
- 🚀 AI is increasingly becoming integral in the technology sector, suggesting a lucrative career path for those in the field.
- 📈 The video presents a 6-month roadmap for learning generative AI, divided into four levels of proficiency: User, Super User, Developer, and Researcher.
- 🛠️ Starting as a User, one should understand popular generative AI tools and gain hands-on experience with them.
- 🔍 Super Users deepen their understanding of generative AI through prompt engineering, a critical skill for effective tool usage.
- 🔧 Developers learn to build and deploy AI applications using APIs and model fine-tuning for domain-specific tasks.
- 💡 Researchers aim to push the boundaries of the generative AI domain itself through in-depth exploration and contribution.
- 🌐 Online AI prompt communities provide valuable examples and feedback for enhancing prompt engineering skills.
- 📚 A solid foundation in programming, particularly Python, is essential for Developers to interact with AI models.
- 🔎 Level One Developers focus on mastering APIs, building QA systems, and understanding the security and limits of API usage.
- 🎓 Level Two Developers require a deeper understanding of advanced topics in machine learning and statistics for fine-tuning models.
- 🏫 The video offers a Generative AI Pinnacle program for a comprehensive learning path and hands-on experience.
Q & A
What is the significance of becoming a super user in the field of generative AI?
-Becoming a super user in generative AI allows individuals to explore and utilize generative AI tools to their full potential, effectively using prompts to shape the AI's responses. This level of proficiency is essential for those aiming to excel in the domain and can significantly enhance the effectiveness of using AI tools for various tasks.
What are some popular generative AI tools mentioned in the script?
-The script mentions Chat GPT, Bard, and Mid Journey as examples of popular generative AI tools that users can employ to gain experience and enhance their skills in the field.
How long does it take to become a super user of generative AI according to the script?
-The script suggests that it takes approximately one month to become a super user of generative AI by learning prompt engineering and its techniques, as well as gaining hands-on experience with various tools.
What are some techniques a developer can learn to enhance their skills in generative AI?
-Developers can learn about API parameters, fine-tuning foundational models, machine learning concepts, deep learning architectures, and frameworks like Lang chain and Llama index to enhance their generative AI skills.
What is the purpose of APIs in generative AI?
-APIs (Application Program Interfaces) serve as a set of defined rules that enable different applications to communicate with each other. In the context of generative AI, APIs allow developers to interact with AI models and deploy their applications.
What are the prerequisites for a developer level one learning journey?
-For a developer level one learning journey, a basic understanding of programming languages, preferably Python, is required. This knowledge is key for interacting with generative AI models via their APIs.
What is the role of parameter-efficient fine-tuning (PFT) in generative AI?
-Parameter-efficient fine-tuning (PFT) is a method that allows for the adoption of pre-trained language models to various downstream applications without fine-tuning all the model's parameters. This makes the deployment of AI models more efficient and less resource-intensive.
What are the steps involved in becoming a level two developer?
-To become a level two developer, one must deepen their understanding of Python, learn advanced topics in probability, statistics, linear algebra, and calculus, dive into machine learning concepts, and gain knowledge of deep learning architectures and frameworks. This level focuses on fine-tuning foundational models for domain-specific tasks.
How can one contribute to the field of generative AI as a researcher?
-As a researcher, one can contribute to the field by building their own generative models from scratch, staying updated with the latest research, participating in online communities and conferences, and sharing their findings with the generative AI community.
What resources are available for someone looking to further their education in generative AI?
-For those looking to further their education in generative AI, there are online communities, research papers, conferences, and specialized programs like the generative AI Pinnacle program mentioned in the script, which offers personalized learning paths, hands-on projects, and mentorship.
What is the generative AI Pinnacle program and how does it benefit those enrolled?
-The generative AI Pinnacle program is an immersive learning experience designed for individuals aspiring to become generative AI experts. It offers a personalized learning roadmap, over 200 hours of learning, real-world projects, weekly one-on-one mentorship with generative AI experts, and access to a variety of generative AI tools and libraries.
Outlines
🚀 Introduction to Generative AI Career Path
This paragraph introduces the viewer to the growing field of Generative AI and its increasing importance in the technology world. It emphasizes the lucrative career opportunities in AI, especially for those interested in generative AI. The video presents a 6-month step-by-step roadmap for learning generative AI, divided into four levels of proficiency: user, super user, developer, and researcher. The assumption is that the viewer already has user-level proficiency with generative AI tools like Chat GPT, Bard, or Mid Journey. The roadmap aims to guide the viewer from a basic user to a super user, then to a developer capable of building and deploying AI applications, and finally to a researcher pushing the boundaries of the generative AI domain.
🌟 Becoming a Super User of Generative AI
This section focuses on the journey to become a super user of generative AI, which involves gaining a deeper understanding of generative AI through prompt engineering. The goal is to use generative AI tools more effectively by exploring their full potential. The viewer is encouraged to learn about prompt engineering theories, the components of an effective prompt, and how to structure prompts to shape AI responses. The importance of hands-on experience with popular generative AI tools is highlighted, as well as the value of participating in online AI prompt communities for better prompting strategies. The paragraph also suggests learning various prompt engineering techniques to optimize the use of generative AI tools.
🛠️ Developer Level One: Building AI Tools
This paragraph outlines the process of transitioning from a super user to a developer level one. It begins with the prerequisites of understanding programming languages, particularly Python, for API interactions with generative AI models. The section covers learning about APIs, their functionality, and how to control model responses using various parameters. It also introduces LLM tools and frameworks like Lang chain and LLAMA index for building QA and RAG systems. The importance of understanding API usage limits, security, and error handling is emphasized. The viewer is guided through building their own AI tools using frameworks like Streamlit or Gradio, and identifying problems to solve with AI, moving towards creating custom solutions.
🔧 Developer Level Two: Fine-Tuning Foundation Models
The paragraph details the next step in the developer journey, focusing on fine-tuning foundation models for domain-specific tasks. It covers the prerequisites of advanced Python knowledge, understanding of probability, statistics, linear algebra, calculus, and machine learning concepts. The section delves into fine-tuning large language models and stable diffusion models, selecting appropriate foundation models, and setting up the fine-tuning environment. It discusses the process of fine-tuning, evaluating model performance, and building custom AI tools using the fine-tuned models. The goal is to develop a deeper understanding of generative AI models and the ability to build custom tools based on them.
🔬 Becoming a Researcher in Generative AI
This final paragraph discusses the path to becoming a researcher in generative AI, which involves building generative models from scratch and contributing to the field. Depending on the chosen track (NLP or computer vision), the viewer is encouraged to learn and implement advanced models, dive deeper into reinforcement learning algorithms, and keep up with the latest research trends. The section highlights the importance of participating in online communities, reading research papers, and attending conferences. It also introduces the Generative AI Pinnacle program for a comprehensive learning path and the Analytics Vidya Community platform for peer learning and industry expert interactions.
Mindmap
Keywords
💡Generative AI
💡Prompt Engineering
💡APIs
💡Fine-tuning
💡LLMs (Large Language Models)
💡Stable Diffusion Models
💡Super User
💡Developer
💡Researcher
💡Generative AI Pinnacle Program
Highlights
AI is becoming the silent architect of our rapidly evolving technology world.
Careers in AI, especially generative AI, are becoming extremely lucrative.
The video provides a 6-month step-by-step roadmap to learn generative AI in 2024.
The roadmap is divided into four proficiency levels: user, super user, developer, and researcher.
To become a super user, one must understand prompt engineering and effective prompt structuring.
Online AI prompt communities can provide good examples of prompts for various use cases.
Developer level one involves learning programming, preferably Python, and understanding APIs.
Mastering APIs of popular generative models like OpenAI's ChatGPT and Google's LaMDA is crucial.
Developer level two requires a deeper understanding of Python, machine learning, and deep learning.
Fine-tuning foundational models for domain-specific tasks is a key aspect of developer level two.
As a researcher, one can delve into building generative models from scratch.
Researchers should stay updated with the latest trends and research in generative AI.
Generative AI Pinnacle program offers a personalized learning roadmap and hands-on projects.
Analytics Vidya AI Community platform provides learning opportunities and access to live webinars.
The video encourages viewers to subscribe for more informative generative AI content.
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