PPT-Gradient-based Learning Applied to Document Recognition

Author : natalia-silvester | Published Date : 2018-11-02

Yann LeCun Leon Bottou Yoshua Bengio and Patrick Haffner 1998 1 Ofir Liba Michael Kotlyar Deep learning seminar 20167 Outline Introduction Convolution neural

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Gradient-based Learning Applied to Document Recognition: Transcript


Yann LeCun Leon Bottou Yoshua Bengio and Patrick Haffner 1998 1 Ofir Liba Michael Kotlyar Deep learning seminar 20167 Outline Introduction Convolution neural network LeNet5. Gradient descent is an iterative method that is given an initial point and follows the negative of the gradient in order to move the point toward a critical point which is hopefully the desired local minimum Again we are concerned with only local op Rights Reserved Page | 85 Volume 2, Issue 5 , May 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. Difference between model-output pressure and pressure obtained by integrating hydrostatic equation (shaded) with in-plane flow vectors (w multiplied by 5), T’(z) in black contours (degrees K), radial outflows in gray contours (m/s).. Yujia Bao. Mar 7, 2017. Finite Difference. Let . be any differentiable function, we can approximate its derivative by. f. or some very small number . ..  . How to compare the numerical gradient . with . 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. Sources: . Stanford CS 231n. , . Berkeley Deep RL course. , . David Silver’s RL course. Policy Gradient Methods. Instead of indirectly representing the policy using Q-values, it can be more efficient to parameterize and learn it directly. Significant progress has been made over the past decade by studies of normal-conducting linear colliders, NLC/JLC and CLIC, to raise achievable accelerating gradient from the range of 20-30 MV/m up to 100-120 MV/m. . Ryota Tomioka (. ryoto@microsoft.com. ). MSR Summer School. 2 July 2018. Azure . iPython. Notebook. https://notebooks.azure.com/ryotat/libraries/DLTutorial. Agenda. This lecture covers. Introduction to machine learning. Topics: . Diffy. , Morph, Gradient Compression. 3D CNNs. Used for video processing. Examining a series of F images in one step. T is typically 3. Note that F reduces as we advance (also because of pooling). in . XFEL . Cryomodule Tests. Denis Kostin, . DESY. . . TTC Topical . Meeting . on SRF Cryomodule Clean Room . Assembly. . 12-14.11.2014. Outline. Cavity Operating Gradient and Degradation. XFEL Module AMTF Test . Andreas Streun, Paul Scherrer Institut, Switzerland. Low emittance rings workshop IV, Frascati, Sep. 17-19, 2014. Contents. Recall: paths to low emittance. Recall: the TME cell. The LGAB cell. Longitudinal gradient bends.

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