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Source channel @githubtrending · Post #14717 · May 18
#jupyter_notebook Learning about Large Language Models (LLMs) can be very beneficial. You can build exciting projects over eight weeks, starting with simple tasks and moving to more complex ones. This journey helps you develop deep expertise in AI and LLMs. You'll learn by doing hands-on projects, which is a fun and effective way to understand how these models work. By the end, you'll have skills that can be used in real-world applications, making it a valuable learning experience. https://github.com/ed-donner/llm_engineering
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@djangoproject · Post #574 · 02/25/2018, 02:34 PM
http://www.paulbrownmagic.com/blog/python_partial_application Python Partial: Code Your Intention Of all the functional programming inspired features in Python, partial application must be the best kept secret that you really need to know. Partial application lets you create highly abstract functions and make them more specific for use, pass a function arguments without calling it yet, and so much more. #tuple#sort
@djangoproject · Post #153 · 09/03/2016, 08:20 PM
http://wla.berkeley.edu/~cs61a/fa11/lectures/streams.html In this chapter, we continue our discussion of real-world applications by developing new tools to process #sequential#data. In Chapter 2, we introduced a sequence interface, implemented in Python by built-in data types such as #tuple and #list. #Sequences supported two operations: querying their length and accessing an element by index. In Chapter 3, we developed a user-defined implementations of the sequence interface, the Rlist class for representing recursive lists. These sequence types proved effective for representing and accessing a wide variety of sequential #datasets.