language learning

Martin Gorner's Devoxx talk introduces deep learning with TensorFlow, walking through neural networks for MNIST, how to choose an architecture and how to diagnose networks that fail to train.

Andrej Karpathy builds a bigram character-level language model from scratch, introducing torch.Tensor and the framework of training, sampling and loss evaluation. It is the first step toward a GPT-style Transformer in his makemore series.

Jeremy Howard's 90-minute practical tour of language models. It covers how they work, GPT-4's capabilities and limits, using the OpenAI API, code writing, data analysis and fine-tuning.

David Sanders gives the first part of a two-part introduction to the Julia programming language at SciPy 2014. It walks through the language's basics and its appeal for scientific computing.

Andrej Karpathy builds a GPT from scratch in code, following Attention Is All You Need and GPT-2/3, in about two hours using PyTorch. He connects it to ChatGPT and assumes familiarity with his earlier makemore videos.

Day 1 of Handmade Hero's introduction to C on Windows, covering setting up Visual Studio, creating a Win32 project, the WinMain entry point and writing a first function. It is the starting point for Casey Muratori's from-scratch game series.

David Beazley's PyCon 2019 tutorial derives the lambda calculus from the ground up in Python, using only single-argument functions. He builds booleans, numerals and more with no other language features.

Jon Gjengset's long live-coding stream explains Rust's Pin and Unpin types: why they exist, how they work, and how they connect to futures and async/await. It also covers moving from pre-pinning code to the new model.

Jon Gjengset's long live-coding Crust of Rust episode on async/await, focused on the mental model for using it rather than on Futures, Pin and Wakers internals. Topics include awaiting multiple futures, cancellation, executors and select.