About this course

Who is this course for?

You: Are a beginner in the field of machine learning or deep learning or AI and would like to learn PyTorch.

This course: Teaches you PyTorch and many machine learning, deep learning and AI concepts in a hands-on, code-first way.

If you already have 1-year+ experience in machine learning, this course may help but it is specifically designed to be beginner-friendly.

What are the prerequisites?

• 3-6 months coding Python.
• At least one beginner machine learning course (however this might be able to be skipped, resources are linked for many different topics).
• Experience using Jupyter Notebooks or Google Colab (though you can pick this up as we go along).
• A willingness to learn (most important).

Video Description

Many programming languages have standard libraries. What about JavaScript? 🤔️

Deno's goal is to simplify programming, and part of that is to provide the JavaScript community with a carefully audited standard library (that works in Deno and Node) that offers utility functions for data manipulation, web-related logic, and more. We created the Deno Standard Library in 2021, and four years, 151 releases, and over 4k commits later, we're thrilled to finally announce that it's 30 modules are finally stabilized at v1.

Learn more about the Deno Standard Library

Read about our stabilization process for the library

Andres Vourakis writes:

Data Scientist Handbook 2024

Curated resources (Free & Paid) to help data scientists learn, grow, and break into the field of data science.

Even though there are hundreds of resources out there (too many to keep track of), I will try to limit them to a maximum of 5 per category to ensure you get the most valuable and relevant resources out there, plus, the whole point of this repository is to help you avoid getting overwhelmed by too many choices. This way you can focus less time researching and more time learning.

FAQs

• How is curation done? Curation is based on thorough research, recommendations from people I trust, and my years of experience as a Data Scientist.
• Are all resources free? Most resources here will be free, but I will also include paid alternatives if they are truly valuable to your career development. All paid resources include the symbol 💲.
• How often is the repository updated? I plan to come back here as often as possible to ensure all resources are still available and relevant and also to add new ones.

There is more to Hindley-Milner type inference than the Algorithm W. In 1988, Didier Rémy was looking to speed up the type inference in Caml and discovered an elegant method of type generalization. Not only it is fast, avoiding scanning the type environment. It smoothly extends to catching of locally-declared types about to escape, to type-checking of universals and existentials, and even to MLF.

Alas, both the algorithm and its implementation in the OCaml type checker are little known and little documented. This page is to explain and popularize Rémy's algorithm, and to decipher a part of the OCaml type checker. The page also aims to preserve the history of Rémy's algorithm.

Read How OCaml type checker works

Book Description

Writing a C Compiler will take you step by step through the process of building your own compiler for a significant subset of C—no prior experience with compiler construction or assembly code needed. Once you’ve built a working compiler for the simplest C program, you’ll add new features chapter by chapter. The algorithms in the book are all in pseudocode, so you can implement your compiler in whatever language you like. Along the way, you’ll explore key concepts like:

• Lexing and parsing: Learn how to write a lexer and recursive descent parser that transform C code into an abstract syntax tree.
• Program analysis: Discover how to analyze a program to understand its behavior and detect errors.
• Code generation: Learn how to translate C language constructs like arithmetic operations, function calls, and control-flow statements into x64 assembly code.
• Optimization techniques: Improve performance with methods like constant folding, dead store elimination, and register allocation.

Compilers aren’t terrifying beasts—and with help from this hands-on, accessible guide, you might even turn them into your friends for life.

Author Bio

Nora Sandler is a software engineer based in Seattle. She holds a BS in computer science from the University of Chicago, where she researched the implementation of parallel programming languages. More recently, she’s worked on domain-specific languages at an endpoint security company. You can find her blog on pranks, compilers, and other computer science topics at https://norasandler.com/.

Table of Arena Crates

For a technical discussion of using arenas for memory allocation with an example implementation, see gingerBill's Memory Allocation Strategies - Part 2: Linear/Arena Allocators

EventHelix writes:

This article will investigate how Rust handles dynamic dispatch using trait objects and vtables. We will also explore how the Rust compiler can sometimes optimize tail calls in the context of dynamic dispatch. Finally, we will examine how the vtable facilitates freeing memory when using trait objects wrapped in a Box.

The blog post is the author's impressions of Gleam after it released version 1.4.0. Gleam is an upcoming language that is getting a lot of highly-ranked articles.

It runs on the Erlang virtual machine (BEAM), making it great for distributed programs and a competitor to Elixir and Erlang (the language). It also compiles to JavaScript, making it a competitor to TypeScript.

But unlike Elixir, Erlang, and TypeScript, it's strongly typed (not just gradually typed). It has "functional" concepts like algebraic data types, immutable values, and first-class functions. The syntax is modeled after Rust and its tutorial is modeled after Go's. Lastly, it has a very large community.

July 17, 2024

Allen B. Downey writes:

Elements of Data Science is an introduction to data science for people with no programming experience. My goal is to present a small, powerful subset of Python that allows you to do real work with data as quickly as possible.

Part 1 includes six chapters that introduce basic Python with a focus on working with data.

Part 2 presents exploratory data analysis using Pandas and empiricaldist — it includes a revised and updated version of the material from my popular DataCamp course, “Exploratory Data Analysis in Python.”

Part 3 takes a computational approach to statistical inference, introducing resampling method, bootstrapping, and randomization tests.

Part 4 is the first of two case studies. It uses data from the General Social Survey to explore changes in political beliefs and attitudes in the U.S. in the last 50 years. The data points on the cover are from one of the graphs in this section.

Part 5 is the second case study, which introduces classification algorithms and the metrics used to evaluate them — and discusses the challenges of algorithmic decision-making in the context of criminal justice.

This project started in 2019, when I collaborated with a group at Harvard to create a data science class for people with no programming experience. We discussed some of the design decisions that went into the course and the book in this article.

Read Elements of Data Science in the form of Jupyter notebooks.

Dmitry Grinberg writes:

go replan all your STM32H7 projects with RP2350, save money, headaches, and time. As a bonus, you’ll get an extra core to play with too! "But," you might say, "STMicro chips come with internal flash, while RP2350 still requires an external SPI chip to store the flash". Hold on to your hats... there are now RP2350 variants with built-in flash! They are called RP2354A nd RP2354B and they include 2MBytes of flash in-package. The pinouts are the same as the RP2350A/B, for a bonus! Why two pinouts? Because the "more GPIOs" dream also came true! There is now a variant with more GPIOS, available in an 80-pin package. That’s right! It is epic!

Read Why you should fall in love with the RP2350