Princeton University - Spring 2016
Deep Learning is the fastest growing area of Machine Learning. As highlighted in New York Times, Deep Learning is the core technique to enable the latest breakthroughs in Computer Vision, Speech Recognition, Robotics, Natural Language Processing, Artificial Intelligence, and Big Data. It uses neural networks many layers deep and large datasets to teach computers how to solve perceptual problems, such as detecting recognizable concepts in data, translating or understanding natural languages, interpreting information from input data, and more. Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition and natural language processing. Practical examples include vehicle, pedestrian and landmark identification for driver assistance; image recognition; speech recognition; natural language processing; neural machine translation and cancer detection. Major high tech companies such as Google, Facebook, Tesla, Microsoft, Intel, Yahoo, Baidu, Apple, NVIDIA, Qualcomm, NEC, Toyota, Huawei, all invest significantly in the area. Students in the seminar will focus on developing core components for deep learning algorithms, or applying deep learning algorithms, such as ConvNet and LSTM to a target application.
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