Forwarded from Tensorflow(@CVision) (Vahid Reza Khazaie)
Cyclical Learning Rates with Keras and Deep Learning
Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.
Reference: https://www.pyimagesearch.com/2019/07/29/cyclical-learning-rates-with-keras-and-deep-learning/
#cyclical_learning_rates #lr #learning_rate
Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.
Reference: https://www.pyimagesearch.com/2019/07/29/cyclical-learning-rates-with-keras-and-deep-learning/
#cyclical_learning_rates #lr #learning_rate
PyImageSearch
Cyclical Learning Rates with Keras and Deep Learning - PyImageSearch
In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for…
Forwarded from The Devs
https://deepmind.com/documents/113/Neuron.pdf
The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent
times, however, communication and collaboration between the two fields has become less commonplace.
In this article, we argue that better understanding biological brains could play a vital role in building intelligent
machines. We survey historical interactions between the AI and neuroscience fields and emphasize current
advances in AI that have been inspired by the study of neural computation in humans and other animals. We
conclude by highlighting shared themes that may be key for advancing future research in both fields.
The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent
times, however, communication and collaboration between the two fields has become less commonplace.
In this article, we argue that better understanding biological brains could play a vital role in building intelligent
machines. We survey historical interactions between the AI and neuroscience fields and emphasize current
advances in AI that have been inspired by the study of neural computation in humans and other animals. We
conclude by highlighting shared themes that may be key for advancing future research in both fields.
A handful of podcasts, labs, projects, and groups which are involved both Neuroscience and Artificial Intelligence:
NeuroAILab: Aim to "reverse engineer" the algorithms of the brain, both to learn about how our minds work and to build more effective artificial intelligence systems.
Learning in Neural Circuits (LiNC) Laboratory: Study general principles of learning and memory in neural networks with the ultimate goal of understanding how real and artificial brains can optimize behaviour.
Human Brain Project: The Human Brain Project (HBP) is building a research infrastructure to help advance neuroscience, medicine and computing. It is one of four FET (Future and Emerging Tehcnology) Flagships, the largest scientific projects ever funded by the European Union.
Center for Brains, Minds and Machines: Understanding how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines is arguably one of the greatest challenges in science and technology. This group brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the Science and Engineering of Intelligence.
Center for Theoretical Neuroscience: they aim to establish, through the quality of the Center's research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding. We believe we have one of the most exciting and interactive environments anywhere for bringing theoretical approaches to Neuroscience.
Unsupervised Thinking: a podcast about neuroscience, artificial intelligence and science more broadly
#NeuroScience #MachineLearning
NeuroAILab: Aim to "reverse engineer" the algorithms of the brain, both to learn about how our minds work and to build more effective artificial intelligence systems.
Learning in Neural Circuits (LiNC) Laboratory: Study general principles of learning and memory in neural networks with the ultimate goal of understanding how real and artificial brains can optimize behaviour.
Human Brain Project: The Human Brain Project (HBP) is building a research infrastructure to help advance neuroscience, medicine and computing. It is one of four FET (Future and Emerging Tehcnology) Flagships, the largest scientific projects ever funded by the European Union.
Center for Brains, Minds and Machines: Understanding how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines is arguably one of the greatest challenges in science and technology. This group brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the Science and Engineering of Intelligence.
Center for Theoretical Neuroscience: they aim to establish, through the quality of the Center's research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding. We believe we have one of the most exciting and interactive environments anywhere for bringing theoretical approaches to Neuroscience.
Unsupervised Thinking: a podcast about neuroscience, artificial intelligence and science more broadly
#NeuroScience #MachineLearning
Chris_Bailey_Hyperfocus__The_New.epub
5.6 MB
A practical guide to managing your attention— a powerful resource you have to get stuff done, become more creative, and live a meaningful life.
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
article:https://arxiv.org/abs/1907.13196v1
arXiv.org
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ --...
An almost new Reinforcement Learning specialization *_*:
https://www.coursera.org/specializations/reinforcement-learning
https://www.coursera.org/specializations/reinforcement-learning
Coursera
Reinforcement Learning
Master the Concepts of Reinforcement Learning. Implement ... Enroll for free.
Wharton's Entrepreneurship Specialization covers the conception, design, organization, and management of new enterprises. This five-course series is designed to take you from opportunity identification through launch, growth, financing and profitability. With guidance from Wharton's top professors, along with insights from current Wharton start-up founders and financiers, you'll develop an entrepreneurial mindset and hone the skills you need to develop a new enterprise with potential for growth and funding, or to identify and pursue opportunities for growth within an existing organization.
https://www.coursera.org/specializations/wharton-entrepreneurship
https://www.coursera.org/specializations/wharton-entrepreneurship
Coursera
Entrepreneurship
Offered by University of Pennsylvania. Turn Your Idea ... Enroll for free.
Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead mainly on unsupervised learning. Here we argue that most animal behavior is not the result of clever learning algorithms—supervised or unsupervised—but is encoded in the genome. Specifically, animals are born with highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is far too complex to be specified explicitly in the genome, it must be compressed through a “genomic bottleneck”. The genomic bottleneck suggests a path toward ANNs capable of rapid learning.
https://www.nature.com/articles/s41467-019-11786-6
https://www.nature.com/articles/s41467-019-11786-6
Nature
A critique of pure learning and what artificial neural networks can learn from animal brains
Nature Communications - Recent gains in artificial neural networks rely heavily on large amounts of training data. Here, the author suggests that for AI to learn from animal brains, it is important...