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- Written by: Babak Abad
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Keras is a popular library for machine learning. This is because a lot of facilities are available with less burdensome in comparison to other ones. In other words, you need less code to train or inference a network, for instance. Statistics for your networks such as current number of epochs, training loss, validation loss, over-fitting prohibition, checkpoints are also available just by a few lines of code or even by couple of words.
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- Written by: Babak Abad
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In this article, we have tried to train a simple neural network using torch (pytorch) library. To keep simplicity, we choose sonar dataset can be downloaded from here. Our dataset is in csv format and all column except the last one are inputs and the final column is output or target column. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
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- Written by: Babak Abad
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Data visualization is an important skill in math, machine learning, or almost any scientific subject. There is as a proof, an image worth more than thousands words.
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- Written by: Babak Abad
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A signal is an electrical impulse or a wave that carries information. This refers to the changing currents, or voltages, or electromagnetic waves that transmit data at any point in electrical systems. Signal processing is an electrical engineering subfield that focuses on analyzing, modifying, and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements.
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- Written by: Babak Abad
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is a branch of artificial intelligence using math or statistical approaches to teach something to a machine, here we talk about computers. In opposition to conventional programming approaches, you can program a computer without thinking about all details. Such programs can learn from experiences instead of traversing line by line of codes. Instead of writing a lot of lines of code to say a machine to do what in different situations, you can just provide several samples.