Now we know that neural networks are a special type of machine learning model. Although, usually these models need huge amounts of data to start outperforming other machine learning approaches, one big advantage is that the process of training neural networks can make use of parallelization in hardware such as graphical processing units (GPUs), which do the operations needed for training neural networks faster than traditional CPUs. This is the reason that in the past few years, new specialized software frameworks have been developed with the capacity to make use of GPUs; examples of these frameworks are Theano, Caffe, and TensorFlow. These frameworks have allowed the deep learning models to be used for professionals outside specialized academic circles, thus democratizing the use of these powerful models. In this section, we introduce the two...
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