Ask HN: How do I learn more about LLMs and ML?

The article suggests a learning path for understanding neural networks. It recommends starting with a book on traditional neural networks to grasp the basics such as what neural networks are, activation functions, and backpropagation. The author then suggests progressing to the numpy user manual, a book about pytorch, a book about transformers, and a book from Wolfram about LLMs, which can all be found on Amazon.

In terms of practical understanding, the author advises implementing a basic picoGPT/Llama/Mistral model from scratch in python & numpy after reading the theory. The author emphasizes the importance of having a strong foundation in math, particularly in matrix-vector multiplications, dot products, and linear algebra.

Key takeaways

Start learning about neural networks with any book on traditional neural networks to understand the basics like activation function and backpropagation.
After understanding the basics, further learning can be done through numpy user manual, a book about pytorch, a book about transformers, and a book from Wolfram about LLMs.
Practical understanding can be achieved by implementing a basic picoGPT/Llama/Mistral model from scratch in python & numpy.
Having a good understanding of matrix-vector multiplications, dot products and linear algebra in general is a must.

Ask HN: How do I learn more about LLMs and ML?

Key takeaways

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