There’s one aspect that has affected the growth of deep learning research — the proliferation of deep learning frameworks. Popular Deep Learning frameworks such as TensorFlow (Google), PyTorch (one of the newest frameworks that is rapidly gaining popularity), Caffe, MXNet and Keras among others have helped DL researchers achieve human-level efficiencies on tasks such as facial recognition, image classification, object detection, sentiment detection among other tasks. While multiple frameworks for deep learning is great news for the developer community, it is also a part of the marketing pitch to get them to lock the developer base into other solutions (selling compute capability).
— Each of these frameworks was designed to solve a specific problem
— After reaching a certain maturity, the frameworks were open sourced
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