What is an agent?
Intro: A thought-provoking essay which sheds new light on the agent-environment boundary and philosophy behind the current definition of agent, especially in the field of reinforcement learning.
https://anna.harutyunyan.net/wp-content/uploads/2020/09/What_is_an_agent.pdf
#reinforcement_learning #philosophy
Intro: A thought-provoking essay which sheds new light on the agent-environment boundary and philosophy behind the current definition of agent, especially in the field of reinforcement learning.
https://anna.harutyunyan.net/wp-content/uploads/2020/09/What_is_an_agent.pdf
#reinforcement_learning #philosophy
Machine Learning & Computational Statistics Course
Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.
https://davidrosenberg.github.io/ml2016/#home
#machine_learning #statistics #course
Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.
https://davidrosenberg.github.io/ml2016/#home
#machine_learning #statistics #course
New Deep Learning Course by Yann LeCun & Alfredo Canziani (Recommended)
Course Intro: This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
Additional Info: This course is available in 11 languages such as Persian, and I personally translated some of the materials of this course to Persian :).
https://atcold.github.io/pytorch-Deep-Learning/
#deep_learning #course
Course Intro: This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
Additional Info: This course is available in 11 languages such as Persian, and I personally translated some of the materials of this course to Persian :).
https://atcold.github.io/pytorch-Deep-Learning/
#deep_learning #course
From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence
Abstract: Despite the recent progress in AI-powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding the right set of inductive biases that make fast learning possible while being general enough to be widely applicable in tasks that humans excel at. To make progress in AGI, we argue that we can look at the human brain for such inductive biases and principles of generalization. To that effect, we propose a strategy to gain insights from the brain by simultaneously looking at the world it acts upon and the computational framework to support efficient learning and generalization. We present a neuroscience-inspired generative model of vision as a case study for such an approach and discuss some open problems about the path to AGI.
URL: https://www.frontiersin.org/articles/10.3389/fncom.2020.554097/full
#neuroscience #artificial_general_intelligence
Abstract: Despite the recent progress in AI-powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding the right set of inductive biases that make fast learning possible while being general enough to be widely applicable in tasks that humans excel at. To make progress in AGI, we argue that we can look at the human brain for such inductive biases and principles of generalization. To that effect, we propose a strategy to gain insights from the brain by simultaneously looking at the world it acts upon and the computational framework to support efficient learning and generalization. We present a neuroscience-inspired generative model of vision as a case study for such an approach and discuss some open problems about the path to AGI.
URL: https://www.frontiersin.org/articles/10.3389/fncom.2020.554097/full
#neuroscience #artificial_general_intelligence
Frontiers
Frontiers | From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence
Despite the recent progress in AI powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, a...
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