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In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks.
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"It's the combination of deep learning, neural network stuff, with reinforcement learning," he explains.
Transfer learning in deep neural networks becomes popular since the success of deep learning in image classification [19].
Choice of deep learning framework underlies this key ideological distinction.
The power of deep learning can be much more accurate.
Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures.
That was harder to do pre-deep learning, so we're catching the front end of deep learning and NL P.
Slow features can be learned using deep learning, which is advantageous for action recognition [2]; however, the effectiveness of deep learning over handcrafted systems is still not evident.
We then describe two main components of deep learning, i.e., deep learning architectures and model optimization.
The main steps of deep learning include building the network architecture, identifying learning goals, and starting learning [18].
The training process can be supervised or unsupervised learning, reinforcement learning, clustering, decision trees or different methods of "deep learning".
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