The GenAItechLab Fellowship program allows participants to work on state-of-the-art, enterprise-grade projects, at their own pace, at home or in their workplace. The goal is to help you test, enhance, and further implement applications that outperform solutions offered by AI startups or organizations such as Google or OpenAI.
You will learn how to quickly build faster and lighter systems that deliver better results based on sound evaluation metrics, with a focus on case studies and best practices. Not the least, you will learn modern methods here to stay, designed by world-class expert and investor, Dr. Vincent Granville, founder of GenAItechLab.com.
With a deep dive into explainable AI, self-tuning apps, multi-LLMs, AI optimization, with new foundational concepts explained in simple English, the program is ideal for developers, software engineers, data scientists, students, analysts, and consultants interested in gaining practical experience in GenAI, building a strong portfolio, or quickly deliver high quality solutions to clients. The training material features new apps built in Granville’s research lab.
Former participants turned some of the projects into Web APIs and Python libraries, found a GenAI engineering job at Meta and other companies, were able to raise VC funding, or simply impressed their boss by delivering better solutions, faster, or solving problems that seemed hopeless at first glance.
FAQ
Feel free to contact us if you don’t find your answer in the following list of questions and answers.
1. How do I enroll, and is there any cost?
Use the 20% discount code MLT12289058 to purchase the project textbook entitled “State of the Art in GenAI & LLMs — Creative Projects, with Solutions”, here. The Python code and datasets are also on GitHub. Some of the projects have been turned into web APIs.
That’s it. To see whether it is suitable for you, read the next questions and answers.
2. What is expected from you?
There is a large selection of projects in the textbook. Choose two of them and complete the steps. Ideally, post your solution on your GitHub repository. Create one (it’s free) if you don’t have one yet. The project textbook (here) has a table of contents at the beginning. It also has glossaries, an index, a modern bibliography, and numerous links to external resources, as well as internal cross-references.
3. Does the fellow title expire after a while?
It does not. However new projects are regularly added to include new developments in the field. A year from now, you may download the updated version of the textbook, and work on new projects.
4. What are the prerequisites?
Basic Python is required to work on the projects, even though the first chapter starts from scratch. Familiarity with basic statistical and machine learning concepts, as well as code and algorithm optimization, also helps. Finally, the program is designed for self-learners: no boring, slow-paced videos here! The only videos are data animations (visualizations) that you create in Python to feature and share results: see examples here.
Once enrolled, if you have specific questions about the projects you are working on, or need personalized guidance or feedback, email Vincent Granville at vincentg@mltechniques.com. See current project list, here.
5. Can I use GPT to solve the projects?
Of course! Also, you can work with other people or use any valuable resources: some are suggested in the first chapter, including the following books from the instructor:
- Statistical Optimization for Generative AI and Machine Learning
- Synthetic Data and Generative AI, with Applications
The goal is not to test your memory, but to help you work as you would in a typical corporate environment, using anything you can to reach the milestones. That said, GPT won’t give you a full solution to any step in any project. But it can help.
6. Are there any exams?
There is no exam. People who succeed at quizzes and brain teasers are not necessarily better than those who don’t. The latter are sometimes the most creative. What matters most: out-of-the box thinking, scalable and efficient solutions, good documentation, re-usable code, and a working implementation (minimum viable product). The project textbook guides you to accomplish this, especially since my solutions are included.
7. How to add the fellowship to my LinkedIn credentials?
To add the GenAItechLab Fellowship in the credential section on your LinkedIn profile, simply click on this link, at any time. It will automatically and immediately update the section in question on your profile. It may also create an announcement shared with your connections and followers. You can manually update the fields before submitting the form, for instance the year and month to show when you started the program.
8. Will it help advance my career or find a job?
Absolutely if you complete working projects showcasing original contributions: better, faster, more general than the solutions in the textbook. Add these projects on GitHub and to your LinkedIn profile to magnify the impact.
9. How qualified is the instructor?
It is almost impossible to find programs featuring state-of-the-art technology along with all the secret sauce, no matter how much you pay. The reason is that experts developing these techniques have signed an NDA with their employer and would be fired and sued if sharing them publicly.
Here you have the opportunity to learn trade secrets from one of the top GenAI leaders in the world, Dr. Vincent Granville, happy to share all his intellectual property for free, thanks to using a different monetization model. Not only that, but the apps that he designs are better than traditional enterprise solutions, precisely because the goal is to fix many issues, for instance: hallucinations, sampling outside the observation range, fast vector search, to name a few. Not via little improvements, but via new foundational, ground-breaking, and visionary technology.
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