Data Science 101: Deep Learning

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Deep learning is a new force in the field of data science – a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. The talk below breaks the ice for those not familiar with this technology. This talk was presented at the SF Neural Network Aficionados Discussion Group hosted by NextSpace in San Francisco.

Adam Gibson (Data Scientist and Co-Founder, presents his open-source, distributed deep-learning framework, Deeplearning4j. He demos sentiment analysis and facial recognition tools. Adam studied computer science and business administration at Michigan Tech, and now serves as machine-learning instructor at the data science academy Zipfian Academy. He lives in San Francisco.

DL4J is a commercial-grade platform written in Java and compatible with Hadoop. Its neural nets work for image recognition, text analysis and speech-to-text. DL4J has implementations of such algorithms as binary and continuous restricted Boltzmann machines, deep-belief nets, denoising autoencoders, convolutional nets and recursive neural tensor networks. Users with a working knowledge of Java will be able to undertake anomaly/fraud detection, recommendation engines and social-media ranking systems, among many other machine learning applications.



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