a digressive series of post and can be skipped.Deep learning in neural networks: An overview Jürgen Schmidhuber AbstractIn recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. jfieb, 9 minutes agoEditDeleteReport #19Reply jfiebMember New Neural Networks for Machine LearningLearn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.About the CourseNeural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.QUIK has a hardcoded FFE, they hard coded a small part of Sensory audio algos, do NOT be surprised that since learning algos will be Ubiquitous and since Ubiquity = a future Eos Engine that QUIK is working on a hard code neural network.It is part of the adjacent possible that QUIK is exploring. FFE = Flexible Fusion engine.This one will be NNLE -neural network learning engine. It will be ubiquitous
a digressive series of post and can be skipped.Deep learning in neural networks: An overview Jürgen Schmidhuber AbstractIn recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Neural Networks for Machine LearningLearn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.About the CourseNeural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.QUIK has a hardcoded FFE, they hard coded a small part of Sensory audio algos, do NOT be surprised that since learning algos will be Ubiquitous and since Ubiquity = a future Eos Engine that QUIK is working on a hard code neural network.It is part of the adjacent possible that QUIK is exploring. FFE = Flexible Fusion engine.This one will be NNLE -neural network learning engine. It will be ubiquitous
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