MIT's Books on AI & ML (FREE DOWNLOAD):
1. Foundations of Machine Learning
https://t.co/78p57EBbL8
2. Understanding Deep Learning
https://t.co/D2oyRrXqcE
3. Introduction to Machine Learning Systems
❯ Vol 1: https://t.co/IezLFJdhDV
❯ Vol 2: https://t.co/NYP3xAPZ6u
4. Algorithms for ML
https://t.co/lntuD4Q19H
5. Deep Learning
https://t.co/vCHVIZQYTI
6. Reinforcement Learning
https://t.co/JNWhFCuCkH
7. Distributional Reinforcement Learning
https://t.co/GXpkV4BDZi
8. Multi Agent Reinforcement Learning
https://t.co/T8zVmQVutO
9. Agents in the Long Game of AI
https://t.co/HeD3Nsm5zz
10. Fairness and Machine Learning
https://t.co/csAjhdf7Lb
11. Probabilistic Machine Learning
❯ Part 1 : https://t.co/5Leef9ypGj
❯ Part 2 : https://t.co/vRbF0rEIuh
DE LA TRAGEDIA DE DOHA A LA REDENCIÓN DE HAJIME MORIYASU
El 28 de octubre de 1993, Japón estaba a solo unos minutos de clasificar a su primera Copa del Mundo. Ganaban 2-1 a Irak cuando un gol iraquí al final del partido acabó con toda la ilusión. 💀
La escena fue devastadora, jugadores derrumbados sobre el césped, aficionados llorando y un país entero sumido en la derrota. Aquella eliminación fue conocida como "La tragedia de Doha".
Entre los jugadores que vivieron esa tragedia estaba un joven mediocampista llamado Hajime Moriyasu. Tenía 25 años y nunca pudo disputar un Mundial como futbolista.
Treinta años después, ese mismo Hajime Moriyasu es quien dirige a una increíble selección japonesa.
La filosofía de Moriyasu nace precisamente de aquella herida.
No es un entrenador de gestos exagerados, no busca protagonismo, no suele levantar la voz.
Su arma favorita es una pequeña libreta en la que toma apuntes durante los partidos, hasta el punto de que los aficionados bromean con que tiene una "Death Note". 🙏
"Maximum, Minimum, and Inflection Points" is a comprehensive introduction to some of the central concepts of differential calculus. The entry develops global and local maxima and minima, critical points, stationary points, and inflection points, showing how derivatives can be used to analyse and classify the behaviour of a function.
These concepts are not limited to calculus. Maximum and minimum points are fundamental to optimisation, where the goal is to identify the best solution among many possible alternatives. The same mathematical principles also underpin many algorithms used in computer science, machine learning, and artificial intelligence.
For example, optimisation methods such as gradient descent iteratively adjust parameters to minimise a loss function, relying on derivatives and the analysis of critical points and local extrema.
https://t.co/RRaCPPCnel
Mathematical bridge between the Time Domain and the Frequency Domain ✍️
The Fourier Transform answers a simple question: "what's inside this signal?".... Think about a musical chord. When you press three keys on a piano at the same time, you hear one combined sound. A trained musician can listen closely and identify each individual note hidden within that chord. That's what the Fourier Transform does, mathematically. It takes any complicated signal and identifies the simple, pure waves inside it, along with their amounts. The diagram illustrates this well. The red, messy wave on the left represents your real-world signal it could be a voice, a heartbeat, or a musical note. The clean blue waves spreading out are the simple components hidden within that signal. The spikes on the right act as a scoreboard a tall spike means that component is a significant part of the signal, while no spike indicates it's absent. The most amazing part is this: "any signal, no matter how rough or complex, can be perfectly reconstructed by adding enough simple smooth waves." A human voice, a stock market chart, or even a picture they are all made of simple ripples layered on top of one another. You can think of it like a prism splitting white light into a rainbow. White light appears as one simple thing, but the prism shows it is made up of many colors mixed together. The Fourier Transform serves as that prism for sound, images, radio signals, or anything else. It uncovers the hidden components that have always been there.