Artificial Intelligent Systems and Machine Learning

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Deep Learning Applications of the Present and Future


     

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Throughout the most recent years Deep learning strategies have been appeared to beat past cutting edge AI procedures in a few fields, with PC vision being quite possibly the most unmistakable cases. This survey paper gives a short outline of the absolute most huge Deep learning plans utilized in PC vision issues, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A concise record of their set of experiences, construction, benefits, and limits is given, trailed by a depiction of their applications in different PC vision undertakings, for example, object identification, face acknowledgment, activity and action acknowledgment, and human posture assessment. At long last, a short outline is given of future headings in planning Deep learning plans for PC vision issues and the difficulties included in that.


Keywords

Deep Learning, Deep learning using Algorithms, Deep Learning Applications
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  • Nando de Freitas’ talks: https://www.youtube.com/user/ProfNandoDF/ videos

  • Christopher Colah’s blog: https://colah.github.io/

  • Andrej Karpathy’s blog: https://karpathy.github.io/

  • Andrej Karpathy’s talks: https://www.youtube.com/channel/UCPk8m_ r6fkUSYmvgCBwq-sw/videos

  • “Deep Learning” (slides by Geoff Hinton, Yoshua Bengio and Yann LeCun, NIPS’2015 tutorial) http://www.iro.umontreal.ca/~bengioy/ talks/DL-Tutorial-NIPS2015.pdf

  • “What’s Wrong with Deep Learning” (slides by Yann LeCun, CVPR’2015 keynote) https://drive.google.com/file/d/0BxKBnD5y2M8NVHRiVXBnOVpiYUk

  • “Deep Learning Tutorial” (slides by Yann LeCun, ICML’2013 tutorial) http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf

  • Deep learning Udacity course: https://classroom.udacity.com/courses/ ud730/lessons/6370362152/concepts/63798118150923

  • Inria deep learning reading group sessions: https://project.inria. fr/deeplearning/sessions/


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  • Deep Learning Applications of the Present and Future

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Abstract


Throughout the most recent years Deep learning strategies have been appeared to beat past cutting edge AI procedures in a few fields, with PC vision being quite possibly the most unmistakable cases. This survey paper gives a short outline of the absolute most huge Deep learning plans utilized in PC vision issues, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A concise record of their set of experiences, construction, benefits, and limits is given, trailed by a depiction of their applications in different PC vision undertakings, for example, object identification, face acknowledgment, activity and action acknowledgment, and human posture assessment. At long last, a short outline is given of future headings in planning Deep learning plans for PC vision issues and the difficulties included in that.


Keywords


Deep Learning, Deep learning using Algorithms, Deep Learning Applications

References