&amp;quot;The future is here. It's just not evenly distributed yet.&amp;quot; - William Gibson :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.>
Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI. The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research.
In the following topics, we will first review techniques used to identify patterns in time series data (such as smoothing and curve fitting techniques and autocorrelations), then we will introduce a general class of models that can be used to represent time series data and generate predictions (autoregressive and moving average models). Finally, we will review some simple but commonly used modeling and forecasting techniques based on linear regression. For more information see the topics below.
Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning. The Deep Learning Summer School 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. Note: Slide synchronization will soon be added.
Implementing a machine learning algorithm in code can teach you a lot about the algorithm and how it works. In this post you will learn how to be effective at implementing machine learning algorithms and how to maximize your learning from these projects. Benefits of Implementing Machine Learning Algorithms You can use the implementation of …
Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the worlds of machine learning and artificial intelligence. We discuss the latest developments in research, technology, and business and explore interesting projects from across the web.
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