💾 Free Deep Learning Course from NASA
We found a practical course on applying modern AI methods in astronomy and astrophysics.
It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.
⛓ Link to the course
https://deeplearning4astro.com/
#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
We found a practical course on applying modern AI methods in astronomy and astrophysics.
It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.
⛓ Link to the course
https://deeplearning4astro.com/
#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
51❤8🔥2
This media is not supported in your browser
VIEW IN TELEGRAM
🔖 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
⛓️ Link to the tutorial
https://enghusseinsheikho.substack.com/p/637-interactive-modules-on-ml-and?r=3hwnr8&utm_campaign=post&utm_medium=web
On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
⛓️ Link to the tutorial
https://enghusseinsheikho.substack.com/p/637-interactive-modules-on-ml-and?r=3hwnr8&utm_campaign=post&utm_medium=web
❤9
The 25 Best Free Resources for Learning AI in 2026
Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.
Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.
Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
❤5