Luis Jiménez

😀

[Luis Jiménez @lengine]

@MATUTEmx Estamos listos para @MATUTEmx el próximo 27 de octubre en la @ArenaCdMexico. Saludos @jorgedalessio y beso a @tanaplanter 😘

[Nutrition Science @NutrioSci]

Excellent summary of Stroke #MedEd #MedX

[Tech Fusionist | Kushal Gangil @techyoutbe]

DevOps & Cloud (Role Based Certification) 👇

++++++

Docker Course for Free👇

[tut_ml @tut_ml]

Best ML Courses- https://t.co/36rgfCCjQm

@KirkDBorne
@antgrasso
@ronald_vanloon
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #SQL #python #AI #ML

[Luis Jiménez @lengine]

@jrisco Yo, un simple radioescucha de las mañanas, deseo que te vaya muy pero muy bien en lo que sigue, que por cierto allí te seguiré escuchando. Animo solo pa enfrente y pa delante !! 🙏

[Madni Aghadi @hey_madni]

🚨 Breaking news:

Google just dropped VLOGGER, and it's crazy.

This is going to transform the future of VIDEO forever

Here’s everything you need to stay ahead of the curve: 🧵 👇

[elvis @omarsar0]

🎓LLM Course

This is such a beautiful and comprehensive resource on LLMs.

It includes notebooks, key references, and roadmaps.

There is something to learn for everyone. For students, researchers, and practitioners.

The Prompt Engineering Guide is also referenced, which is cool to see.

One observation as I was reviewing the references is how much hard work the ML community dedicates toward open and high-quality education. This resource does a great job of organizing all those incredible LLM educational resources that exist out there.

One topic I would add is LLMOps. But to be fair, the majority of the topics are roughly covered in the LLM Engineer Roadmap.

Highly recommended!

And last but not least, many thanks to @maximelabonne for releasing this excellent resource. 👏

[Kirk Borne @KirkDBorne]

[Free 601-page PDF download] Mathematical Introduction to #DeepLearning — Methods, Implementations, and Theory: https://t.co/jxW43uTdJG
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#AI #Mathematics #BigData #DataScience #Algorithms #MachineLearning #Calculus #LinearAlgebra #DataScientists

[Frank Nielsen @FrnkNlsn]

For my new followers:
"Mathematics for machine learning" (CUP, 2020).

🎉Free PDF from https://t.co/mbzGgyoAVP

😊A must-read masterpiece, nicely structured and richly color-illustrated!

[Dan Goodman @neuralreckoning]

I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by @MarcusGhosh. YouTube Videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.

[Oscar Arias @OACerebro]

📚 Este libro se basa en apuntes de una serie de conferencias y prácticas impartidas durante varios años en la Universidad Aristóteles de Tesalónica, Grecia.

🔬 Puede ser un material de apoyo sobre estadísticas básicas en medicina utilizando R. También es útil para autoaprendizaje de estudiantes e investigadores en el campo biomédico.

🎓 Dirigido a estudiantes universitarios con formación en ciencias (ingeniería, matemáticas) que deseen adentrarse en las ciencias biomédicas.

📘 El libro se estructura en dos partes principales: "Parte 1: Fundamentos de R" y "Parte 2: Estadísticas". La primera ofrece una visión general de R, enfoques comunes para el manejo de datos con funciones amigables de {dplyr} y guías paso a paso para visualizar datos con {ggplot2}.

📊 La segunda parte aborda las pruebas estadísticas más comunes con ejemplos del campo biomédico. Se presentan funciones estadísticas de Base R y del paquete complementario {rstatix} en paralelo en la mayoría de los ejemplos para involucrar al lector y enriquecer la experiencia de codificación. #Ciencia #Estadísticas #RProgramming

🍁 https://t.co/CLgLxyMk62

[Jean de Nyandwi @ ✈️🌁COLM 26 @Jeande_d]

TinyML and Efficient Deep Learning Computing, MIT 2023

A course that covers efficient AI techniques used for deploying deep learning models on resource-constrained devices.

The topics covered include model compression, pruning, quantization, neural architecture search, distributed training, data/model parallelism, gradient compression, on-device fine-tuning, and applications specific techniques for large language models, diffusion models, and video recognition.

Large language(and image) models are remarkably great at tasks they do but getting them to actually work require a huge amount of computational resources. It's nice to see courses that are dedicated for demystifying large models deployments.

Lecture videos: https://t.co/tkz2KzBboI

Website: https://t.co/28GUaq9LN1

[Giuseppe Cavaliere @CavaliereGiu]

Econ Nobel prize winner 𝐓𝐨𝐦 𝐒𝐚𝐫𝐠𝐞𝐧𝐭 (@nyuniversity & @HooverInst) about which #math courses to take if you study #economics.

Written for NYU and @Stanford students - but the key message applies to everyone.

Have a look! https://t.co/bVrEJWuSfw

#EconTwitter

[Mushtaq Bilal, PhD @MushtaqBilalPhD]

Practical AI for Instructors and Students: A Free Course

Lilach Mollick and Ethan Mollick at the U of Pennsylvania have developed a five-part course on how we can use AI in the classroom.

It's totally free and full of helpful resources.

Check it out 👇
https://t.co/l2b2NTlRz5

[Frank Nielsen @FrnkNlsn]

🆓 PDF of excellent book draft on machine learning (slated for MIT Press)
"Learning Theory from First Principles" by Francis Bach.
👉https://t.co/wv8Rbk4QV2

Francis Bach is keynote speaker at
"Geometric Science of Information" (GSI'23, 30th Aug-1st Sept)
https://t.co/ywxhjTIRsU

[Frontiers - Neuroscience @FrontNeurosci]

New Research: Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging: Introduction

Decoding brain activities is one of the most popular topics in neuroscience in recent years.… #Neuroscience https://t.co/Kf5PGnfm6r

[Bennett Landman (Vanderbilt MASI Lab) @MASI_Lab]

Our SynB0 distortion correction has been very well received for correcting spatial problems in diffusion weighted MRI when reverse phase encoded scans or field maps are not available. (This "should" not be a problems, but all too often is...). We now release a version for fMRI!/1

[Kirk Borne @KirkDBorne]

Download 244-page PDF “Introduction to #NeuralNetworks”
https://t.co/CWVdswSt7H via @MikeE_3_14
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#BigData #DataScience #AI #Algorithms #MachineLearning #DeepLearning

[Cal Short @calconverts]

AI is a liar.

For article writers, this is a huge problem.

So I built Reword, an article editor that has fact-checking and citations built in.

We did what ChatGPT couldn't.

Don't believe me? Try it for free...

[PythoNeuro @pythoneuro]

Braindecode is a #Python toolbox for decoding raw electrophysiological brain data with deep learning models. The tutorials are great for anyone who wants to dive in!
@agramfort @robintibor @BAristimunha @KEggensperger @FrankRHutter @wolfram_burgard #neuroscience #OpenScience

[Maxime Labonne @maximelabonne]

Just launched a step-by-step guide to Large Language Models.

Perfect for beginners & experts seeking to deepen their knowledge - from basics of ML/NLP to instruction fine-tuning and weight quantization. Every step backed by curated resources.

💻 GitHub: https://t.co/hsdjIpgJ0R