Python string methods look simple, but they power real data cleaning, validation, and text processing. lower, split, replace, find, and join are used daily in analytics and automation workflows. Mastering them saves hours of manual effort. #Python#DataAnalysis#Programming #Automation
Applications are open for the School on Analytical and Numerical Methods for Disordered Quantum Systems, focused on key topics in Beyond the Standard Model phenomenology.
"Computing Neural Network Gradients" is a clear introduction to the mathematics behind backpropagation and gradient computation in neural networks.
Published as part of Stanford CS224N, these notes walk through the chain rule, computational graphs, vectorised derivatives, and efficient gradient calculations with concrete examples rather than black-box formulas.
Gradients are at the heart of modern deep learning. Every parameter update in a neural network, from a small classifier to today's Transformer-based LLMs, ultimately relies on backpropagation and efficient gradient computation.
https://t.co/vQ9xoTDSYD
one of the quotes i find most inspiring on a hard day:
"Whatever your hand finds to do, do it with all your might, for in the realm of the dead, where you are going, there is neither working nor planning nor knowledge nor wisdom"
Ecclesiastes 9:10
🔗 GitHub: https://t.co/o9YBasKIQT
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Good experimental design is the foundation of research that holds up. ABI and the African Plant Breeders Association are hosting a free, open Agri-Informatics Lecture Series from June 2026. Register for each session separately.
https://t.co/I1Riw8rIa1 👇
Genç bilim insanlarına küçük bir tavsiye…
Makalelerinize uygun dergi bulmakta zorlanıyorsanız aşağıdaki araçları kullanmanızı öneririm;
1️⃣Springer Journal Suggester
https://t.co/S8BU2Zam1Z
2️⃣Elsevier Journal Finder
https://t.co/bgGUGeK72u
3️⃣Match my manuscpt (Web of Science)
https://t.co/TRuoYxmCrs
Makalenizin başlığını, özetini ve anahtar kelimelerini girerek uygun dergi önerileri alabilirsiniz🌺
🔗 GitHub: https://t.co/HSWSPs0KJQ
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✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd
🔗 GitHub: https://t.co/Lm0Ly7dIPX
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✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd
A must-read survey to refresh math and gen AI basics → The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
It shows a clear walkthrough of how gen AI learns to understand, model, and create complex data, covering:
- Latent algebra foundations: PCA, SVD, autoencoders
- Latent models: PPCA and VAEs
- VAEs: ELBO, inference, reparameterization
- Diffusion: the way from noise → denoising
- Score-based and continuous-time generative modelling
- Density models: flows, autoregression
- GANs and energy-based models beyond likelihoods