Getting Started with Deep Learning

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1. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were available, we could not find a concise summary document to reference for starting a new deep learning project.
   
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3. One way to give back to the open source community that provides us with tools is to help others evaluate and choose those tools in a way that takes advantage of our experience. We offer the chart below, along with explanations of the various criteria upon which we based our decisions.

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关键词：Learning earning getting Started Start different available reference learning creating

1. CNN Modeling Capability: Convolutional neural networks (CNNs) are used for image recognition, recommendation engines, and natural language processing. A CNN is composed of a set of distinct layers that transform the initial data volume into output scores of predefined class scores (For more information, check out Eugenio Culurciello’s overview of Neural Network architectures). CNN’s can also be used for regression analysis, such as models that output of steering angles in autonomous vehicles. We consider a technology’s CNN modeling capability to include several features. These features include the opportunity space to define models, the availability of prebuilt layers, and the tools and functions available to connect these layers. We’ve seen that Theano, Caffe, and MXNet all have great CNN modeling capabilities. That said, TensorFlow’s easy ability to build upon it’s InceptionV3 model and Torch’s great CNN resources including easy-to-use temporal convolution set these two technologies apart for CNN modeling capability.

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1. RNN Modeling Capability: Recurrent neural networks (RNNs) are used for speech recognition, time series prediction, image captioning, and other tasks that require processing sequential information. As prebuilt RNN models are not as numerous as CNNs, it is therefore important if you have a RNN deep learning project that you consider what RNN models have been previously implemented and open sourced for a specific technology. For instance, Caffe has minimal RNN resources, while Microsoft’s CNTK and Torch have ample RNN tutorials and prebuilt models. While vanilla TensorFlow has some RNN materials, TFLearn and Keras include many more RNN examples that utilize TensorFlow.

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1. Keras Compatible: Keras is a high level library for doing fast deep learning prototyping. We’ve found that it is a great tool for getting data scientists comfortable with deep learning. Keras currently supports two back ends, TensorFlow and Theano, and will be gaining official support in TensorFlow in the future. Keras is also a good choice for a high-level library when considering that its author recently expressed that Keras will continue to exist as a front end that can be used with multiple back ends.

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