Today, I want to take you along on a ride through my journey into AIML and Generative AI. It’s been a vortex of curiosity, and lots of learning with some creativity and motivation. I want to share part of this with you in this short article! Some of my favorite resources are shared at the end!
Buckle up, let’s dive in!
First steps into AIML
Some time ago, I had a look at the AIML world. The possibilities were really interesting and it looked like a good challenge to start learning and exploring some material.
Picture this: me, diving into Machine Learning with some rest of my mathematical background.
I started with Coursera’s Standford course & the AWS ML Specialty certification to give me a guideline on how to progress.
Think of it as a treasure hunt for tech gems with the cert path as a map!
🔗 https://coursera.org/specializations/deep-learning
🔗 https://aws.amazon.com/fr/certification/certified-machine-learning-specialty/
GenAI fun
Then after passing the cert and gaining some experience with hands-on projects, the AIML rabbit hole led me right into GenAI.
I explored a bit and thought: “It’s like coding but with a splash of magic!”. The use cases sounded really interesting and promising.
When AIML meets GenAI, think peanut butter & jelly, but way cooler! Seriously, it’s the sweet spot where theory, creativity and code unite. At first, it’s all about AI creating art, crafting stories or composing music. Then you start to see awesome use cases:
📚 Education
🍃 Sustainability
🏥 Medical and Healthcare
👩🦯 Assistive and inclusive technology
🧑🔬 Research
🗓️ Productivity
🎶 Art and Creativity
🎬 Entertainment
And a lot of other fields…
Let’s build some knowledge
Later, I enrolled in the “Generative AI with LLMs” on Coursera to apply some deeper knowledge on the AWS landscape. I highly recommend it, this course is really a “must-follow” for any AI enthusiast! 👇
https://www.coursera.org/learn/generative-ai-with-llms
It tackles GenAI lifecycle for projects, transformer architecture, model fine-tuning, performance optimization, responsible AI challenges and even some reinforcement learning topics.
Oh, and speaking of resources, let’s not forget the amazing platforms that made my AIML and GAI journey even more incredible. Dive into these articles and communities that’ll help you understand the GenAI world in (nearly) no time👇
Learning resources
🔗 https://jalammar.github.io
📖 https://oreilly.com/library/view/generative-deep-learning/9781098134174/
🔗 https://huggingface.co/docs
🔗 https://deeplearning.ai/courses/
🔗 https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/generative-ai/
🔗 https://a16z.com/2023/05/25/ai-canon/
🔗 https://promptingguide.ai
🔗 https://huyenchip.com/2023/06/07/generative-ai-strategy.html
🎉 Ready for more AIML and GenAI goodness?
From unique topics to innovative projects, I want to hear YOUR ideas! 🤔
Knowledge is best when shared so if you enjoyed this post, feel free to connect and share this post with others who might find it exciting!
Feel free to reach me on Twitter FlolightC to keep me updated about your AWS machine learning and Generative AI journey or to ask me questions! I’m always happy to discuss with you!
