MLOps Lecture Series 02 - Serving ML models using VPS

About this video

Editorial review · 85% confidence

Round-10 hunt: picked; channel-level screens (sellers/exam/re-upload/devotional/vendor/events) and provenance check passed; strict per-title check + final strict pass; oEmbed verified.

A 138-minute video from Theja Tulabandhula: “MLOps Lecture Series 02 - Serving ML models using VPS”.

Programming & CS

Video by Theja Tulabandhula, played through YouTube's embedded player.

2:32:34

Statistical Models Lec 06 MLE Bootstrap and Review Fall 2017 Theja TulabandhulaProgramming & CS

1:28:45

Statistical Models Lec 05 LDA Logistic Regression Model Selection Fall 2017 Theja TulabandhulaProgramming & CS

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

Intro25.1M views

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

Intro4.41M views

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

8.21M 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

2:27:41

CS50x 2024 - Lecture 1 - C CS50Programming & CS

Intro1.41M views

2:04:55

CS50x 2024 - Lecture 0 - Scratch CS50Programming & CS

Intro2.2M views

“Hello, world” from scratch on a 6502 — Part 1 Ben EaterProgramming & CS

5.85M views

Ruby Conf 12 - Y Not- Adventures in Functional Programming by Jim Weirich ConfreaksProgramming & CS

65.7K views

1:13:50

The Continuity of Splines Freya HolmérProgramming & CS

1.89M views

2:32:34

Statistical Models Lec 06 MLE Bootstrap and Review Fall 2017

Theja Tulabandhula

A 153-minute video from Theja Tulabandhula: “Statistical Models Lec 06 MLE Bootstrap and Review Fall 2017”.

1:28:45

Statistical Models Lec 05 LDA Logistic Regression Model Selection Fall 2017

Theja Tulabandhula

A 89-minute video from Theja Tulabandhula: “Statistical Models Lec 05 LDA Logistic Regression Model Selection Fall 2017”.

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.

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.

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'.

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.

2:27:41

CS50x 2024 - Lecture 1 - C

CS50

Intro1.41M views

CS50x 2024 lecture 1: an introduction to C — syntax, types, conditionals, loops and compiling.

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.

“Hello, world” from scratch on a 6502 — Part 1

Ben Eater

5.85M views

Building a computer around the 6502 processor on a breadboard and running 'Hello, world'.

Ruby Conf 12 - Y Not- Adventures in Functional Programming by Jim Weirich

Confreaks

65.7K views

Jim Weirich derives the Y combinator live, step by step, using nothing but Ruby lambdas, and along the way teaches the refactorings of lambda calculus.

1:13:50

The Continuity of Splines

Freya Holmér

1.89M views

A follow-up on splines: the different kinds of continuity (C and G), and how Hermite, Catmull-Rom, B-splines and NURBS trade them off.