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  • Optimizing your LLM in production

    Note: This blog post is also available as a documentation page on Transformers. Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however: To exhibit near-human text unders

      Optimizing your LLM in production
    • Awesome - Most Cited Deep Learning Papers | Curated list of awesome lists | Project-Awesome.org

      [Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017. A curated list of the most cited deep learning papers (2012-2016) We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to p

      • Dynamic Programming is not Black Magic - Quentin Santos

        This year’s Advent of Code has been brutal (compare the stats of 2023 with that of 2022, especially day 1 part 1 vs. day 1 part 2). It included a problem to solve with dynamic programming as soon as day 12, which discouraged some people I know. This specific problem was particularly gnarly for Advent of Code, with multiple special cases to take into account, making it basically intractable if you

          Dynamic Programming is not Black Magic - Quentin Santos
        • Unification-free ("keyword") type checking

          From my perspective, one of the biggest open problems in implementing programming languages is how to add a type system to the language without significantly complicating the implementation. For example, in my tutorial Fall-from-Grace implementation the type checker logic accounts for over half of the code. In the following lines of code report I’ve highlighted the modules responsible for type-che

            Unification-free ("keyword") type checking
          • Transformer models: an introduction and catalog — 2023 Edition

            Transformer models: an introduction and catalog — 2023 Edition January 16, 2023 52 minute read This post is now an ArXiV paper that you can print and cite. Update 05/2023 Another pretty large update after 4 months. I was invited to submit the article to a journal, so I decided to enlist some help from some LinkedIn colleages and completely revamp it. First off, we added a whole lot of new models,

              Transformer models: an introduction and catalog — 2023 Edition
            • Vim9 script for Python Developers · GitHub

              vim9script4pythondevelopers.md Vim9 script for Python Developers Vim9 script�Vim script��������������������������������������������������系��� def������義����������Vim script��vim9script�����使����������(vim9script���

                Vim9 script for Python Developers · GitHub
              • Why APL is a language worth knowing

                “A language that doesn't affect the way you think about programming, is not worth knowing.”, by Alan J. Perlis. Why APL is a language worth knowing Alan Perlis, the computer scientist recipient of the first Turing award, wrote “A language that doesn't affect the way you think about programming, is not worth knowing.” ― Alan J. Perlis, 1982. Special feature: Epigrams on programming. ACM Sigplan Not

                  Why APL is a language worth knowing
                • Reindex, Transform, and Aggregate datasets using pandas Library

                  Most of the time, the dataset we will get from the business will be dirty and cannot be used straight forward to train machine learning models. Therefore, we must treat the dataset and bring it to the desired form to input it into an algorithm. This tutorial discusses reindexing, transforming, and aggregating datasets in Pandas. What are Reindexing, Transforming, and Aggregating?Reindexing, transf

                    Reindex, Transform, and Aggregate datasets using pandas Library
                  • The AI-Native Software Engineer

                    An AI-native software engineer is one who deeply integrates AI into their daily workflow, treating it as a partner to amplify their abilities. This requires a fundamental mindset shift. Instead of thinking “AI might replace me” an AI-native engineer asks for every task: “Could AI help me do this faster, better, or differently?”. The mindset is optimistic and proactive - you see AI as a multiplier

                      The AI-Native Software Engineer
                    • Python Interview Questions

                      Here is a list of common Python interview questions with detailed answers to help you prepare for the interview as a Python developer. Python, with its versatile use cases and straightforward syntax, has seen its popularity growing continuously in software development, data science, artificial intelligence, and many other fields. As such, interviews for Python-related positions are designed not on

                        Python Interview Questions
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