This is The Entire Computer Science Curriculum in 1000 YouTube Videos
This is The Entire Computer Science Curriculum in 1000 YouTube Videos In this article, we are going to create an entire Computer Science curriculum using only YouTube videos. The Computer Science curriculum is going to cover every skill essential for a Computer Science Engineer that has expertise in Artificial Intelligence and its subfields, like: Machine Learning, Deep Learning, Computer Vision,
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
I see people make the same mistakes over and over again when learning Rust. Here are my thoughts (ordered by importance) on how you can ease the learning process. My goal is to help you save time and frustration. Let Your Guard Down Stop resisting. That’s the most important lesson. Accept that learning Rust requires adopting a completely different mental model than what you’re used to. There are a
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
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
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
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,
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As Elixir's Machine Learning (ML) ecosystem grows, many Elixir enthusiasts who wish to adopt the new machine learning libraries in their projects are stuck at a crossroads of wanting to move away from their existing ML stack (typically Python) while not having a clear path of how to do so. I would like to take some time to talk to WHY I believe now is a good time to start porting over Machine Lear
GitHub isn't only a rendition control administration; it is a marvelous substance asset for all-things-advancement. From free digital books and instructional exercises, to talk with planning material and 'amazing' bullet point articles, GitHub is the go-to learning center for Developers anxious to up-expertise themselves and stay important. A great deal of designers love to invest energy on 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
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
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
本記事は、Yang Zhou氏による「8 Levels of Using List Comprehension in Python」（2020年10月14日公開）の和訳を、著者の許可を得て掲載しているものです。 8つのレベルでPythonのリスト内包表記を使ってみる Photo by Jason Cooper on Unsplash はじめに リスト内包表記は、とてもPythonらしい技術であり、コードをとても洗練されたものにできる技術です。ただし構文は、特に初心者や他言語経験者のプログラマには少し分かりにくいです。リスト内包表記に関する資料を沢山読みましたが、正直なところ、これがどれほど強力で美しいか完璧に説明しているものはありませんでした。そのような訳でこの記事があります😊 この記事では、初歩から奥義まで、8つのレベルで内包表記を使用する方法を紹介します。 全てのレベルを理解すれば、
"The closer to the train station, the worse the kebab" - A "Study" - James Pae
This write-up was originally posted on reddit, though I've cleaned things up specifically for this post. Due to reasons discussed towards the end of this post, I'm not entirely happy with the results and intend to take another shot at it in the near future. Introduction🔗 I came across this post sharing a hypothesis from a French subreddit; The closer to the train station, the worse the kebab. The
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
NumPy library on Python is an essential tool for data scientists to work on numerical data, especially when they deal with data arrays, especially multi-dimensional, and need a memory-efficient fast indexing of arrays, However, knowing about other useful packages when solving data science problems is essential. So, let’s see which packages are available in Python programming language and are used
v2022: Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, and Stella Biderman. Original: Sasha Rush. The Transformer has been on a lot of people’s minds over the last year five years. This post presents an annotated version of the paper in the form of a line-by-line implementation. It reorders and deletes some sections from the original paper and adds comments throughout. This docume
In Data Science, we primarily use Python as a programming language to perform operations on the available datasets. This article will discuss concepts and details for using Pythons to simplify data operations in data science. Pros and Cons of Python for Data OperationsEven though the pros outweigh the cons, it is crucial to look at both aspects. So, let’s have a look at the advantages and limitati
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Optimizing your LLM in production
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Transformer models: an introduction and catalog — 2023 Edition
Vim9 script for Python Developers · GitHub
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The AI-Native Software Engineer
8つのレベルでPythonのリスト内包表記を使ってみる - Qiita
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