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MIT Introduction to Deep Learning This week, MIT launched its 2025 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof. Ava Amini. The course started this week and will run until May. The course lectures are published weekly. The course

⭐️⭐️ Lecture 2 for MIT Intro to Deep Learning 2025 now online!! ✅ Sequence models 🔄 ✅ Attention 👀 ✅ Transformers 🤖 🧠 New lecture every Monday at 11am ET! 🔥 Watch #FREE here 👉 youtube.com/watch?v=GvezxU… 👀 Sign up for updates 👉 youtube.com/channel/UCtslD… 📅 Website 👉

⭐️⭐️ Lecture 3 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL. Topics include CNNs, vision 👀, images/video 📸, and more! 🔥 Lecture 👉 youtube.com/watch?v=oGpzWA… 🤩 New lecture every Monday 10am ET w/ slides & TensorFlow PyTorch Labs labs! 🌐 Website 👉

⭐️⭐️ Lecture 4 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL. Learn the fundamentals of #GenerativeAI! 🧠 🔥 Lecture 👉 youtube.com/watch?v=SdTZAM… 🤩 New lecture every Monday 11am ET w/ slides & TensorFlow PyTorch labs! 🌐 Website 👉 IntroToDeepLearning.com

⭐️⭐️ Lecture 5 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL. Learn super-human decision making w/ #ReinforcementLearning! 🔥 Lecture 👉 youtube.com/watch?v=to-lHJ… 🤩 New lecture every Monday 11am ET w/ TensorFlow PyTorch labs! 🌐 Website 👉

⭐️⭐️ Lecture 6 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL. 🔥 NEW content on language models #LLMs, #GenAI, #AI for Bio, and more! 🧠 🔥 Lecture 👉 youtube.com/watch?v=HLKo4f… 🤩 New lecture every Monday 11am ET w/ TensorFlow PyTorch labs! 🌐 Website 👉

⭐️⭐️ Lecture 7 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL! Learn about the foundations of working with #LLMs with Peter Grabowski from Google, from prompt engineering to agents and everything in between! Part 1 of 2 on #LLMs! Stay tuned for more! 🔥

⭐️⭐️ Lecture 8 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL! Learn the secrets of #LLM #PostTraining with Maxime Labonne from Liquid AI ! You don’t want to miss this absolute master class! 🔥 Lecture 👉 youtube.com/watch?v=_Hfdnc… 💻 LLM Software Lab 👉

MIT literally packed 60 minutes with everything you need to know about LLMs

⭐️⭐️ Lecture 9 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL! Should there be a Hippocratic Oath for #AI? Tune in to hear about this from the amazing Doug Blank of Comet! 🚀 🔥 Lecture 👉 youtube.com/watch?v=CyCUZA… 🌐 Website 👉 IntroToDeepLearning.com

⭐️⭐️ Lecture 10 of MIT Intro to Deep Learning 2025 is now available online #FREE for ALL! 🧬 Learn about the cutting edge of AI for Biology with Ava Amini from Microsoft Microsoft Research! Tune in to learn how AI can learn the language of nature to help us understand and treat disease 🧪

Honored to present the grand awards at #ISEF2025 on behalf of Liquid AI! #RegeneronISEF is the largest science research competition in the world. This year at ISEF: 🎓 over 1,600 finalists 🌎 over 60 countries worldwide 💰 over $9 million USD of prizes Liquid AI sponsored

EvoDiff is now available on Microsoft Azure AI Foundry! ai.azure.com/labs/projects/… 🧬 AI for generating novel proteins to accelerate biological discovery, together with more breakthrough models from Microsoft Research! 👉 ai.azure.com/labs

The final version of our work on enabling protein-function annotation on new, unseen functions with ProtNote is now published, with code and models available! 📰 doi.org/10.1093/bioinf… 💻 github.com/microsoft/prot… with Samir Char, Nate Corley, Sarah Alamdari, Kevin K. Yang 楊凱筌

MIT literally packed 60 minutes with everything you need to know about LLMs

Next Tues (6/3) at 4PM ET, we will have Ava Amini present "Deep learning guided design of protease substrates" Paper: biorxiv.org/content/10.110… Sign up on our website for zoom links!

Do protein language models store different structural elements in factorizable subnetworks? To find out, we masked out PLM weights to suppress performance on CATH subcategories or secondary structure elements while maintaining performance on other sequences or residues.