Welcome to Generative AI Genius! This 20-day introductory course is designed to help you break into generative AI. This course is designed for today's busy individuals who crave concise, succinct information.
Generative AI Genius the first AI course based on short videos or reels!
If you've been wanting to learn about generative AI, understand the buzzwords, and not feel lost, you're in the right place. You can spend as little as 2-5 minutes a day learning generative AI in a way that builds on the knowledge gained from previous videos, giving you a comprehensive mind-map of the field.
I see each of you as one of the following types of learners:
1. The Busy Bee
If you're short on time but want to grasp generative AI concepts, my videos/reels are perfect for you. Just dedicate 2-5 minutes daily, and you'll stay informed without needing to look up extra material. Concepts will build on each other, keeping you perfectly in the loop.
2. The Curious Learner
If you liked the the videos but want to explore the concepts further, I've handpicked some great resources for you. These usually take about 20-30 minutes and will help you grasp the material more deeply. They'll also improve your understanding of related concepts, making everything more cohesive.
3. The Hands-On Enthusiast
If you're someone with a coding background who prefers hands-on learning, I'll be sharing few mini-project resources throughout the course. These projects will allow you to put the concepts into practice, using high-quality tutorials and videos.
🚨NOTE: The videos stand alone, so you can understand the concepts without needing to read the additional resources—they're just there to aid your understanding.
This course heavily focuses on applied generative AI to help you get started with building applications. Here's an overview of the topics we'll cover, and if you don't understand some of these, don't worry—you'll get enough background during the course:
- Basics of Generative AI and Large Language Models (LLMs)
- Prompting Techniques
- Building Generative AI Applications (RAG)
- Basics of Fine-Tuning
- Common Challenges and Evaluation
- Future Trends in Generative AI
Please note that this course emphasizes understanding applied concepts and building applications using generative AI. It won't teach you to build generative AI models, which requires a much more comprehensive course structure and a lot of prerequisites. If someone tells you otherwise, I'd double-check their credentials 🙂
Honestly, this is a course I want people from all backgrounds to engage with and take away valuable insights at their preferred level of understanding. However, the amount of information you can absorb may vary depending on your background.
Here’s what it offers to individuals with different backgrounds:
The course may introduce terms that are new to you and some parts might be challenging. However, you'll still gain a high-level overview of the field and understand key concepts. Based on my experience, you should be able to grasp about 60-80% of the content. It's still worth your 2-5 minutes daily, right?
If you're a software engineer or tech enthusiast, you probably have a basic understanding of ML concepts such as training and evaluation. You should be able to follow the course from beginning to end and complete a few projects in generative AI. My primary audience consists of individuals like you who are seeking to enter the field. This course can also serve as your entry into building generative AI projects and transitioning to a career as a generative AI engineer.
If you have experience in ML but are new to NLP or LLMs, the main advantage for you will be the mini-projects and supplementary reading materials. These resources should provide you with enough knowledge to begin implementing your own projects and also enable you to start studying generative AI research and understand its broader context.
You have two options: auditing the course or registering, both of which are free!
Auditing the Course:
- You can watch the daily videos first on Instagram & YouTube. Links will be posted here too, but there might be a delay. For quicker updates, follow my account.
- Access additional resources through this page daily; mini-project resources will also be available here.
Registering For the Course:
- Registrations are closed for this course, feel free to audit (watch videos and access content here)
- Registered participants will receive all the benefits mentioned above, plus
- Priority RSVP to a 1 hour seminar on building generative AI applications for the real world
- Updates regarding any future courses or events
- A completion certificate
Aishwarya Naresh Reganti works as a tech lead at the AWS-Generative AI Innovation Center in California, where she leads projects aimed at building production-ready generative AI applications for medium to large-sized businesses. With over 8 years of experience in machine learning, Aishwarya has published 30+ research papers in top AI conferences and mentored numerous graduate students. She actively collaborates with research labs and professors from institutions like Stanford University, University of Michigan, and University of South Carolina on projects related to LLMs, graph models and generative AI.
Outside her professional and academic pursuits, Aishwarya actively contributes to education through various channels. She offers free courses online, with over 3000 individuals having taken them already, and serves as a guest instructor at institutions like Massachusetts Institute of Technology and University of Oxford.
Additionally, she co-founded The LevelUp Org in 2022, a tech mentoring community dedicated to assisting newcomers in the field through mentorship programs and career-oriented events. A recognized industry expert and thought leader, Aishwarya frequently speaks at various industry conferences like ODSC, WomenTech Network, ReWork, and AI4, and has presented research at top-tier AI research conferences including EMNLP, AAAI, and CVPR.
LinkedIn: https://www.linkedin.com/in/areganti/
Instagram: https://www.instagram.com/aish_reganti/
YouTube: https://www.youtube.com/@aishwaryanr4606
The videos will be available here everyday at 7:30 PM, Pacific Time
Key Topics: AI, Generative AI, Neural Networks, Large Language Models(LLMs), Model Training
Reading Material:
Key Topics: Training, Fine-Tuning, Reinforcement Learning, Alignment
Reading Material:
Key Topics: Prompting, Prompt Engineering
Reading Material:
Key Topics: Chain of Thought Prompting, Self-Refine, Self-Consistency, Zero-Shot, Few-Shot
Reading Material:
- https://www.promptingguide.ai/techniques
- [Optional] The Prompt Report: A Systematic Survey of Prompting Techniques
Key Topics: Meta Prompting, Automatic Prompt Engineeering
Reading Material:
- Claude Meta Prompting Engine
- https://cobusgreyling.medium.com/automatic-prompt-engineering-907e230ece0
Key Topics: Hallucinations, Sycophancy, Causes of Hallucinations
Reading Material:
In this mini-project, you'll complete a 1 hour course from Deeplearning.AI that can help you build a chatbot that does the following
- Summarizing (e.g., summarizing user reviews for brevity)
- Inferring (e.g., sentiment classification, topic extraction)
- Transforming text (e.g., translation, spelling & grammar correction)
- Expanding (e.g., automatically writing emails)
Prerequisites: Familiarity with Python
The course is completely free for everyone to take. Please find it here
Happy coding!!
Key Topics: Definition, Needle in a haystack test, Lost in the middle problem
Reading Material:
Key Topics: Basics, 4 Phases of RAG
Reading Material:
Key Topics: Word Vectors/Embeddings, Semantic Similarity, Embeddings in RAG
Reading Material:
Key Topics: Embeddings, Vector databases, Similarity search
Reading Material:
Key Topics: Evaluation Dimensions
Reading Material:
Key Topics: Definition, Resources
Reading Material:
- https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/fine-tuning
- https://www.superannotate.com/blog/llm-fine-tuning
Notebooks and Coding Courses/Tutorials:
- https://github.com/aishwaryanr/awesome-generative-ai-guide?tab=readme-ov-file#fine-tuning-tutorials
- Training & Fine-Tuning LLMs for Production by Activeloop
- Finetuning Large Language Models by DeepLearning.AI
- Tutorial to Fine-Tune Mistal on your own data by Brev.Dev
Apps to generate AI videos (like the one I created)
Key Topics: Definition, Alignment, Reward model
Reading Material:
Reading Material:
Key Topics: Planning, Memory, Tools
Reading Material:
Key Topics: Jailbreaking, Attacks
Reading Material:
- https://www.discovermagazine.com/technology/adversarial-attack-makes-chatgpt-produce-objectionable-content
- Jailbreaking paper shown in the video (link)
Key Topics: SLMs, Multimodal Models, Agents, Embodied AI
Reading Material:
- https://www.forbes.com/sites/janakirammsv/2024/01/02/exploring-the-future-5-cutting-edge-generative-ai-trends-in-2024/
- https://vmblog.com/archive/2023/12/14/kognic-2024-predictions-the-year-of-embodied-ai.aspx
Key Topics: Knowledge distillation, Pruning, Quantization
Reading Material:
Key Topics: AI Engineer Skill Checklist
Key Topics: AI Engineer Interview Structure