http://www.meetup.com/Ankara-Deep-Learning/events/225100127/
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Yazar: Ferhat Kurt
NVIDIA Deep Learning Institute Sertifikalı eğitmen.
Makale: Can Deep Learning Help You Find The Perfect Match?
Is he/she my type or not? The answer to this question depends on the personal preferences of the one asking it. The individual process of obtaining a full answer may generally be difficult and time consuming, but often an approximate answer can be obtained simply by looking at a photo of the potential match. Such approximate answers based on visual cues can be produced in a fraction of a second, a phenomenon that has led to a series of recently successful dating apps in which users rate others positively or negatively using primarily a single photo. In this paper we explore using convolutional networks to create a model of an individual’s personal preferences based on rated photos. This introduced task is difficult due to the large number of variations in profile pictures and the noise in attractiveness labels. Toward this task we collect a dataset comprised of 9364 pictures and binary labels for each. We compare performance of convolutional models trained in three ways: first directly on the collected dataset, second with features transferred from a network trained to predict gender, and third with features transferred from a network trained on ImageNet. Our findings show that ImageNet features transfer best, producing a model that attains 68.1% accuracy on the test set and is moderately successful at predicting matches.
Makale: Visualizing and Understanding Convolutional Networks
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.
Sunum: Developing Neural Networks Using Visual Studio
A neural network is an artificial intelligence technique that is based on biological synapses and neurons. Neural networks can be used to solve difficult or impossible problems such as predicting which team will win the Super Bowl or whether a company’s stock price will go up or down. In a short and informal session, Dr. James McCaffrey, from Microsoft Research in Redmond, WA, will describe exactly what neural networks are, explain the types of problems that can be solved using neural networks, and demonstrate how to create neural networks from scratch using Visual Studio. You will leave this session with an in-depth understanding of neural networks and get some early information about a related, soon-to-be-released Microsoft product.
Makale: Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled ‘seed’ image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101).