PPT-Natural Gradient Works Efficiently in Learning

S Amari 110318Fri Computational Modeling of Intelligence Summarized by Joon Shik Kim Abstract The ordinary gradient of a function does not represent its steepest direction but the natural gradient does. Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Machine Learning. Large scale machine learning. Machine learning and data. Classify between confusable words.. E.g., {to, two, too}, {then, than}.. For breakfast I ate _____ eggs.. “It’s not who has the best algorithm that wins. . G.Anuradha. Review of previous lecture-. Steepest Descent. Choose the next step so that the function decreases:. For small changes in . x. we can approximate . F. (. x. ):. where. If we want the function to decrease:. David Kauchak. CS 451 – Fall 2013. Admin. Assignment 5. Math background. Linear models. A strong high-bias assumption is . linear . separability. :. in 2 dimensions, can separate classes by a line. Lecture 4. September 12, 2016. School of Computer Science. Readings:. Murphy Ch. . 8.1-3, . 8.6. Elken (2014) Notes. 10-601 Introduction to Machine Learning. Slides:. Courtesy William Cohen. Reminders. Yann . LeCun, Leon Bottou, . Yoshua Bengio and Patrick Haffner. 1998. . 1. Ofir. . Liba. Michael . Kotlyar. Deep learning seminar 2016/7. Outline. Introduction . Convolution neural network -. LeNet5. Applications. Lectures 12-13: . Regularization and Optimization. Zhu Han. University of Houston. Thanks . Xusheng. Du and Kevin Tsai For Slide Preparation. 1. Part 1 Regularization Outline. Parameter Norm Penalties. Goals of Weeks 5-6. What is machine learning (ML) and when is it useful?. Intro to major techniques and applications. Give examples. How can CUDA help?. Departure from usual pattern: we will give the application first, and the CUDA later. Prajit Ramachandran. Outline. Optimization. Regularization. Initialization. Optimization. Optimization Outline. Gradient Descent. Momentum. RMSProp. Adam. Distributed SGD. Gradient Noise. Optimization Outline. 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. Jiang. Feb 17. Model formulation.  .  .  .  .  .  . …. Recall the model of fully-connected neural networks.  . When .  . Linear Networks. In the following slides, we only consider linear networks without bias:. This book is about diseases treatable with vitamins. It is also about any number of other ways in which you can, as I say, fire your doctor. Should you ever want to put someone to sleep, just start lecturing on nutrition with the ever-boring vitamins A through E and foods that contain them approach. I guarantee that heads will be nodding long before you finish with the B complex.....Andrew Saul, Ph.D., is contributing editor for the Journal of Orthomolecular Medicine. A biologist and teacher by training, Dr. Saul has been a consulting specialist in natural healing for more than twenty-five years, helping medical doctors\' problem patients get better. He has taught thousands of students at New York Chiropractic College and the State University of New York. Dr. Saul\'s previous book, Paperback Clinic, has been used as both a college textbook and reference work for health practitioners. He lives and practices in upstate New York. 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. v. v. v. v. Shared weights. Filter = ‘local’ perceptron.. Also called . kernel.. Yann . LeCun’s. MNIST CNN architecture. DEMO. http://scs.ryerson.ca/~aharley/vis/conv/. Thanks to Adam Harley for making this..