The below course by Dhaval Patel is a beginner-level course for Deep Learning in Python with Tensorflow 2.0 and Kares. The course covers the foundations of neural network and deep learning, which includes the following topics: 🧵👇🏼
Only 2 days left to get your student application in!! Neuromatch Academy can be a huge career boost for people looking to improve their computational skills. #neuroscience#neuroai#deeplearning
Registration is open, but act fast! 🚨🚨
Sunday, March 24th (every time zone on Earth) is our Priority Deadline. While applications will be accepted until March 31st, those who register by the priority deadline will receive higher consideration for acceptance. Don't delay! Secure your spot and be part of our amazing courses. Register on neuromatch.io #neuroscience#deeplearning#neuroai
If I need to describe data science in one word, it would be optimization, and in two words, convex optimization. Convex optimization is the mathematical mechanizing beyond many data science algorithms, from least squares to neural network. The Convex Optimization course by Prof. Stephen Boyd (Stanford University) focuses on methods for identifying and solving convex optimization problems.
I don't think the tech nerds out there understand how upsetting generative AI is to artists. Not because it will replace them, but because there will be a generation of soulless creation devoid of humanity.
Also, how many children are looking at the progress and thinking 'what's the point of becoming an artist?'. Or how many school directors are thinking 'what's the point of a fine art budget'.
En 2018, #AlphaFold, l’IA créée par #DeepMind, faisait une percée spectaculaire. [...] Une IA capable de prédire à quoi ressemblera une #protéine à partir de sa seule séquence. Sur les 200 millions de protéines connues, nous ne connaissons la structure 3D que de 20 % d'entre elles. Le #deeplearning fait donc cette promesse : combler ces lacunes pour de grandes avancées médicales et la création de nouveaux #médicaments
Yesterday, Amazon released a new open-source project, Chronos - a family of pre-trained time series forecasting models based on language model architectures.
It is introductory, and very very basic, but still I am a bit nervous as it is not my field, I've just used bits and bobs from it here and there. Hopefully there are no CS students rolling their eyes 😅
(1/2) Foundation Models & Generative AI Course - MIT Course 🚀
The course by Rickard Brüel Gabrielsson is a crash course on foundation models. This is the second version of the course, and it covers topics such as:
✅ Introduction to foundation models
✅ Different algorithms (ChatGPT, Stable-Diffusion & Dall-E)
✅ Supervised learning
✅ Neural networks
✅ Reinforcement learning
✅ Self-supervised learning
✅ Auto-encoders
The ML for Beginners is a course by Microsoft that covers the foundation of machine learning. As the name implies, this 12-week course is for beginners and provides intro to the following topics:
✅ Fairness and machine learning
✅ Regression and classification
✅ Clustering
✅ Natural language processing
✅ Time series forecasting
✅ Reinforcement learning
(1/2) Apple open source a new Python library for simulation framework for accelerating research in Private Federated Learning.
The library - pfl is a Python framework developed at Apple to empower researchers to run efficient simulations with privacy-preserving federated learning (FL) and disseminate the results of their research in FL.
Enrollment is now open for teaching assistant positions at Climatematch Academy 2024 😀! Join us for Computational Tools for Climate Science 2024 from July 15 - 26th 💻.
TA positions available 📣.
Enroll on the neuromatch.io website before March 24th.
Just spent at least two hours deleting all of my work from Tumblr, before their AI scraping shit hits the fan, although it's probably too late. In that case, the deletion functions as a gesture of protest.
This shameless large-scale intellectual property theft by greedy tech business assholes everywhere is starting to make the internet pretty annoying. 😖
This lecture focuses on the following topics:
✅ Optimized Matrix Multiplication
✅ Shared Memory Techniques for CUDA
✅ Implementing Shared Memory Optimization
✅ Translating Python to CUDA and Performance Considerations
✅ Numba: Bringing Python and CUDA Together