#graph #courses

#graph #courses

#audio #petproject

Робот задолбался (с озвучкой)

Напиши монолог уставшего робота, который задолбался сортировать посылки на конвейерной ленте. Добавь описание голоса и эмоций в квадратных скобках на английском. Например, так: [angry sigh]. А сам монолог на русском. Что-то в духе: "Так, а это что за бумажка? [sighs] Ладно, отсканируем.

Read aloud in a tired mechanical robot tone:

[Tired voice] Так, а это что за бумажка? [Sigh] Ладно, отсканируем. Блин, кто посылает бумажки? [Wearily] ладно, идем дальше. Посылка. Пакет. [With interest] Так, а это что у нас? Вы серьезно? Зачем вы посылаете пустые пакеты?! Пофиг. В общую кучу, полежи там.

Дальше. Опять пакет. [Mechanical sigh] Как же я задолбался...

Опять мусор. [Angrily] Да вы издеваетесь?! Сами разгребайте свой мусор!

[Very angry] ВСЁ! С МЕНЯ ХВАТИТ!

23 часа в смену! За эти сущие копейки?! [Sighs] К черту всё! [Tiredly] А я ухожу в датацентр, буду картинки генерировать.

Для Gemini-TTS нужен ВПН, у меня заработал только с американским IP.

Робот: Figure 02
LLM: gemini-2.5-pro-preview-06-05, https://lmarena.ai/
TTS: Gemini Pro 2.4 Preview TTS, голос Orus, https://aistudio.google.com/generate-speech
финальная анимация: Wan-14b-i2v, 720x720x49f, 3090

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#ml #books

Introduction to Machine Learning by Laurent Younes

This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning.

It starts with an introductory chapter that describes notation used throughout the book and serve at a reminder of basic concepts in calculus, linear algebra and probability and also introduces some measure theoretic terminology, which can be used as a reading guide for the sections that use these tools. The introductory chapters also provide background material on matrix analysis and optimization. The latter chapter provides theoretical support to many algorithms that are used in the book, including stochastic gradient descent, proximal methods, etc.

After discussing basic concepts for statistical prediction, the book includes an introduction to reproducing kernel theory and Hilbert space techniques, which are used in many places, before addressing the description of various algorithms for supervised statistical learning, including linear methods, support vector machines, decision trees, boosting, or neural networks.

The subject then switches to generative methods, starting with a chapter that presents sampling methods and an introduction to the theory of Markov chains.

The following chapter describe the theory of graphical models, an introduction to variational methods for models with latent variables, and to deep-learning based generative models.

The next chapters focus on unsupervised learning methods, for clustering, factor analysis and manifold learning.

The final chapter of the book is theory-oriented and discusses concentration inequalities and generalization bounds.

Links:
- arXiv
- pdf

Navigational hashtags: #armbooks
General hashtags: #ml #machinelearning #optimization #regression #classification #nn #neuralnetworks #trees

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