How to get from high school math to cutting-edge ML/AI
The roadmap for learning deep learning includes four stages: foundational math, classical machine learning, deep learning, and advanced techniques like transformers and large language models, ensuring comprehensive understanding and skills development.
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the situation, they often assumed a level of prior knowledge that many learners did not possess. To effectively learn deep learning, it is essential to have a solid grasp of the foundational math and classical machine learning concepts. Once these are in place, learners can explore deep learning through structured courses and textbooks that provide a comprehensive understanding of neural networks, their architectures, and their applications. Recommended resources include specialized online courses and textbooks that focus on practical implementations and theoretical foundations. Finally, the fourth stage involves delving into cutting-edge machine learning techniques, such as transformers and large language models (LLMs), which require familiarity with both deep learning and classical machine learning principles. This roadmap aims to guide learners from basic math to advanced ML/AI concepts, equipping them with the necessary skills to engage with contemporary research and applications in the field.
- The roadmap consists of four stages: foundational math, classical machine learning, deep learning, and cutting-edge machine learning.
- Foundational math includes algebra, calculus, linear algebra, probability, and statistics.
- Classical machine learning focuses on implementing basic models and understanding their underlying principles.
- Deep learning involves mastering multi-layer neural networks tailored to specific tasks.
- The final stage covers advanced techniques like transformers and LLMs, essential for engaging with current ML/AI research.
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5 commentsthe rest of the journey is to find an ml course which acts like a survey of the current state of the art. this field has complexity due to abstraction and horrendous naming practices. to understand a given paper requires working your way in reverse from concepts around it.
in addition, learn a "maths in code" platform of your choice to map the concepts to something you can run.
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