François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=Bo8MY4JpiXE&t=173s
#machine_learning #artificial_intelligence #podcast
https://www.youtube.com/watch?v=Bo8MY4JpiXE&t=173s
#machine_learning #artificial_intelligence #podcast
YouTube
François Chollet: Keras, Deep Learning, and the Progress of AI | Artificial Intelligence Podcast
François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with d...
A great and comprehensive review of meta-learning algorithms
https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
#meta_learning #deep_learning #machine_learning
https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
#meta_learning #deep_learning #machine_learning
Lil'Log
Meta Learning
A set of tutorials and courses on Geometric Deep Learning:
https://people.lu.usi.ch/bronstem/teaching_tutorial.html
#deep_learning #geometric_deep_learning #machine_learning
https://people.lu.usi.ch/bronstem/teaching_tutorial.html
#deep_learning #geometric_deep_learning #machine_learning
Lectures Slides of Signal Processing for Machine Learning Course by Stanfrod University
http://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
http://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
Solving Rubik’s Cube with a Robot Hand
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
Openai
Solving Rubik’s Cube with a robot hand
We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic…
A must read document for deep learning & machine learning practitioners
https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning
https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning
Self-training with Noisy Student improves ImageNet classification
New state-of-the-art supervised+unsupervised algorithm on ImageNet
https://arxiv.org/abs/1911.04252
#machine_learning #neural_networks #meta_learning
New state-of-the-art supervised+unsupervised algorithm on ImageNet
https://arxiv.org/abs/1911.04252
#machine_learning #neural_networks #meta_learning
arXiv.org
Self-training with Noisy Student improves ImageNet classification
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which...
How Relevant is the Turing Test in the Age of Sophisbots?
Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?
https://arxiv.org/pdf/1909.00056.pdf
#machine_learning #technology #ethics
Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?
https://arxiv.org/pdf/1909.00056.pdf
#machine_learning #technology #ethics