Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged. In this paper, we propose a novel yet extremely simple method Virtual Softmax to enhance the discriminative property of learned features by injecting a dynamic virtual negative class into the original softmax. Injecting virtual class aims to enlarge inter-class margin and compress intra-class distribution by strengthening the decision boundary constraint. Although it seems weird to optimize with this additional virtual class, we show that our method derives from an intuitive and clear motivation, and it indeed encourages the features to be more compact and separable. This paper empirically and experimentally demonstrates the superiority of Virtual Softmax, improving the performances on a variety of object classification and face verification tasks.

We identify and study two common failure modes for early training in deep ReLU nets. For each, we give a rigorous proof of when it occurs and how to avoid it, for fully connected, convolutional, and residual architectures. We show that the first failure mode, exploding or vanishing mean activation length, can be avoided by initializing weights from a symmetric distribution with variance 2/fan-in and, for ResNets, by correctly scaling the residual modules. We prove that the second failure mode, exponentially large variance of activation length, never occurs in residual nets once the first failure mode is avoided. In contrast, for fully connected nets, we prove that this failure mode can happen and is avoided by keeping constant the sum of the reciprocals of layer widths. We demonstrate empirically the effectiveness of our theoretical results in predicting when networks are able to start training. In particular, we note that many popular initializations fail our criteria, whereas correct initialization and architecture allows much deeper networks to be trained.

Python image recognition sounds exciting, right? However, it can also seem a bit intimidating. There's no need to be scared! This tutorial will teach you Python basics and how to use TensorFlow. Take this chance to discover how to code in Python and learn TensorFlow linear regression then apply these principles to automated Python image recognition. Through this course, you'll master Python image recognition software and learn with hands-on examples.

In this tutorial, you will learn how to apply deep learning to perform medical image analysis. Specifically, you will discover how to use the Keras deep learning library to automatically analyze medical images for malaria testing. Such a deep learning medical imaging system can help reduce the 400,000 deaths per year caused by malaria. Today's tutorial was inspired by two sources. They've helped me as I've been studying deep learning. I live in an area of Africa that is prone to disease, especially malaria. I'd like to be able to apply computer vision to help reduce malaria outbreaks. Do you have any tutorials on medical imaging? I would really appreciate it if you wrote one.

Subject to stringent regulatory regimes with considerable legacy complications hindering new infrastructure, technology, or processes -- most hospitals are also saddled with significant bureaucracy, and a general fear of change. These facts contribute to a reality in which healthcare organizations are expected to be downright laggardly where it comes to modern business practices and supporting ...

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Photos of an artificial intelligence textbook for Chinese preschoolers have gone viral. Artificial Intelligence Experiment Materials is a 33-volume textbook series aimed at Chinese students from kindergarten to high school that was published this July by Henan People's Publishing House. AI researchers from Google, the Institute of Automation of the Chinese Academy of Sciences, and key Chinese universities collaborated on the textbooks, which pertain to an AI education initiative launched this July by the China Education Technology Association Smart Learning Committee and UNESCO. The aim is to democratize AI education in 100 Chinese schools, introduce pre-teens to the basics, strengthen teenagers' capability for using intelligent and applied technologies, and help train hundreds of new AI teachers. Also included in the initiative is a cloud-based AI e-learning platform that students can access via PC or WeChat.

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I am excited to announce the newest course from deeplearning.ai, It will be available on Coursera in early 2019. AI is not only for engineers. This non-technical course will help you understand technologies like machine learning and deep learning, and spot opportunities to apply AI to problems in your own organization. You will see examples of what today's AI can -- and cannot -- do.