The BigDL library provides users with the ability to run deep learning applications on the Apache Spark framework while leveraging Math Kernel Library (MKL)—which consists of optimized mathematical operations that constitute the basis of machine learning algorithms—to boost performance. BigDL is ideal for training complex networks on large, distributed datasets on commodity CPUs and allows you to extend your existing work by importing models from TensorFlow, Caffe, Torch, and Keras.
Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL’s components and explaining how to use it to implement machine learning algorithms. You’ll use your newfound knowledge to build algorithms that make predictions using real-world datasets. These include simple linear and logistic regression models as well as deep learning algorithms like multilayer perceptron networks, convolutional neural networks, recurrent neural networks, and autoencoders.
Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.
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