[HN Gopher] Hands-On Large Language Models
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       Hands-On Large Language Models
        
       Author : teleforce
       Score  : 134 points
       Date   : 2025-04-19 01:52 UTC (21 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | relyks wrote:
       | If someone's familiar with this, what would you say are the
       | prerequisites?
        
         | saqrais wrote:
         | This is taken from the book as it is:
         | 
         | Prerequisites This book assumes that you have some experience
         | programming in Python and are familiar with the fundamentals of
         | machine learning. The focus will be on building a strong
         | intuition rather than deriving mathematical equations. As such,
         | illustrations combined with hands-on examples will drive the
         | examples and learning through this book. This book assumes no
         | prior knowledge of popular deep learning frameworks such as
         | PyTorch or TensorFlow nor any prior knowledge of generative
         | modeling. If you are not familiar with Python, a great place to
         | start is Learn Python, where you will find many tutorials on
         | the basics of the language. To further ease the learning
         | process, we made all the code available on Google Colab, a
         | platform where you can run all of the code without the need to
         | install anything locally.
        
       | samchon wrote:
       | I came in thinking it was a free ebook lol
        
         | triyambakam wrote:
         | Well it can be... <<Arkhiv Anny>>
        
       | d_tr wrote:
       | I guess it wouldn't sell shit if it used a language suitable for
       | this type of work.
        
         | d_tr wrote:
         | I mean, what happened to "use the right tool for the job"?
         | There is Rust, C++, Julia, D, and certainly many more. Are they
         | hard or what? Are they harder than mastering the math and
         | algorithms that are relevant to an LLM? The code is actually
         | pretty simple, certainly simpler than many "boring" apps.
        
           | antononcube wrote:
           | I assume you mean book's code shown in the Jypyter notebooks
           | in the repository. (Which I think is both simple and messy.)
        
           | simonw wrote:
           | Arguing that Rust, C++, Julia or D are a better "right tool
           | for the job" than Python for a book that teaches people about
           | LLMs is a bit of an eyebrow-raiser.
        
             | d_tr wrote:
             | How so? Since when is Python a good language for numerical
             | computation? What if the reader wants to try something that
             | cannot be achieved by plumbing canned C++? They are out of
             | luck I guess.
             | 
             | Good job teaching the sloppy semantics of a scripting
             | language for numerics I guess.
        
               | simonw wrote:
               | "Since when is Python a good language for numerical
               | computation?"
               | 
               | 30 years. Numeric came out in 1995, then evolved into
               | NumPy in 2005. https://en.m.wikipedia.org/wiki/NumPy
               | 
               | Almost every AI researcher and AI lab does most of their
               | research work in Python.
        
               | d_tr wrote:
               | I know all of these facts. Doesn't mean it is how it is
               | for the right reasons, and even if it is, it does not
               | imply that it is a good way to teach.
        
               | simonw wrote:
               | Taking constant side-quests into Rust memory management
               | during a class on LLMs doesn't sound like a productive
               | way to teach to me.
        
               | sokoloff wrote:
               | It is possible that the vast majority of AI researchers
               | are flat-out incorrect and need to be shown a better
               | direction by you.
               | 
               | It is also possible that your own fitness-for-purpose
               | coefficients are tuned differently than the majority of
               | the field and they've made a sensible choice for their
               | situation.
               | 
               | I'd wager on the latter.
        
           | belter wrote:
           | Your comment shows such a fundamental misunderstanding of how
           | modern AI/LLM works that is hard to be kind and
           | thoughtful....
           | 
           | Python is simply the orchestration layer. The heavy lifting
           | is done by low-level libraries used in the repo, written in
           | C++, CUDA, and Rust (e.g., PyTorch's C++ backend, Flash
           | Attention's CUDA kernels, FAISS's SIMD optimizations, or
           | Hugging Face's Rust-powered tokenizers).
           | 
           | Python's role is to glue these high-performance components
           | together with minimal overhead, while providing
           | accessibility. Claiming it's "unsuitable" ignores the entire
           | stack beneath the syntax.
           | 
           | A critique that is like blaming the steering wheel for a
           | car's engine performance.
        
             | d_tr wrote:
             | Again, I am extremely well aware of all of this. You
             | assumed too much.
        
       | qwertox wrote:
       | Official code repo for the O'Reilly Book - "Hands-On Large
       | Language Models"
       | 
       | No text of the book in there.
        
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       (page generated 2025-04-19 23:02 UTC)