Deploy Python ML Apps

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Full-course playlist 'Machine Learning for Engineers' from a curated course list (Machine Learning for Engineers 2022 (YouTube)); uploader is instructor; playlist judged: Instructor-led university-affiliated course teaching practical machine learning for engineers with substantive worked examples and tutorials.; title passed a strict title check; oEmbed verified.

What you'll learn

A 23-minute video from APMonitor.com: “Deploy Python ML Apps”.

Programming & CS

Video by APMonitor.com, played through YouTube's embedded player.

12:37

Local RAG LLM with Ollama APMonitor.comProgramming & CS

16:48

Hyper-Parameter Optimization APMonitor.comProgramming & CS

But what is a neural network? | Deep learning chapter 1 3Blue1BrownScience & Math

Intro25.1M views

Let's build GPT: from scratch, in code, spelled out.Andrej KarpathyProgramming & CS

8.21M views

The spelled-out intro to neural networks and backpropagation: building micrograd Andrej KarpathyProgramming & CS

Intro4.41M views

Transformers, the tech behind LLMs | Deep Learning Chapter 5 3Blue1BrownProgramming & CS

Intro11.6M views

24:26

The Beauty of Bézier Curves Freya HolmérProgramming & CS

2.23M views

Lecture 1A: Overview and Introduction to Lisp MIT OpenCourseWareProgramming & CS

Intro421K views

8 Bit & '8 Bitish' Graphics-Outside the Box GDC Festival of GamingArt & Design

Advanced568K views

What the heck is the event loop anyway? | Philip Roberts | JSConf EU JSConfProgramming & CS

3.8M views

2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

Intro2.2M views

1:01:39

"Simple Made Easy" - Rich Hickey (2011)Strange Loop ConferenceProgramming & CS

Advanced220K views

12:37

Local RAG LLM with Ollama

APMonitor.com

A 13-minute video from APMonitor.com: “Local RAG LLM with Ollama”.

16:48

Hyper-Parameter Optimization

APMonitor.com

A 17-minute video from APMonitor.com: “Hyper-Parameter Optimization”.

But what is a neural network? | Deep learning chapter 1

3Blue1Brown

Intro25.1M views

Builds up what a neural network actually is — neurons as numbers, layers, weights and biases — using handwritten-digit recognition as the running example.

Let's build GPT: from scratch, in code, spelled out.

Andrej Karpathy

8.21M views

Building a GPT-style language model from scratch in code, following 'Attention Is All You Need'.

The spelled-out intro to neural networks and backpropagation: building micrograd

Andrej Karpathy

Intro4.41M views

Building micrograd, a tiny autograd engine, to understand backpropagation completely.

Transformers, the tech behind LLMs | Deep Learning Chapter 5

3Blue1Brown

Intro11.6M views

How transformer models (the 'T' in GPT) turn text into vectors and predict the next token, step by step.

24:26

The Beauty of Bézier Curves

Freya Holmér

2.23M views

Freya Holmér builds Bézier curves from linear interpolation up, covering de Casteljau's algorithm, derivatives, arc length and their use in games and graphics.

Lecture 1A: Overview and Introduction to Lisp

MIT OpenCourseWare

Intro421K views

Lecture 1A of Abelson and Sussman's 1986 Structure and Interpretation of Computer Programs course: what computer science is about, and an introduction to Lisp.

8 Bit & '8 Bitish' Graphics-Outside the Box

GDC Festival of Gaming

Advanced568K views

Mark Ferrari shows how 8-bit era artists used palette cycling, dithering and tile tricks to get rich images out of tiny palettes.

What the heck is the event loop anyway? | Philip Roberts | JSConf EU

JSConf

3.8M views

Philip Roberts explains the JavaScript runtime: call stack, web APIs, the callback queue and the event loop, with a visualiser that makes asynchronous execution concrete.

2:04:55

CS50x 2024 - Lecture 0 - Scratch

CS50

Intro2.2M views

CS50x 2024 lecture 0: how computers represent information, algorithms, and first programs in Scratch.

1:01:39

"Simple Made Easy" - Rich Hickey (2011)

Strange Loop Conference

Advanced220K views

Rich Hickey distinguishes 'simple' (not intertwined) from 'easy' (familiar), and argues for designing software by reducing complexity rather than chasing convenience.