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With all the buzz about deep learning and artificial neural networks, haven’t you always wanted to create one for yourself? In this book, we’ll create models to carry out two tasks. The first will recognize handwritten digits, while the second will carry out facial recognition.
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An artificial neural network is a mathematical model that converts a set of inputs to a set of outputs through a number of hidden layers. An ANN works with hidden layers, each of which is a transient form associated with a probability. In a typical neural network, each node of a layer takes all nodes of the previous layer as input. A model may have one or more hidden layers.
ANNs receive an input layer to transform it through hidden layers. An ANN is initialized by assigning random weights and biases to each node of the hidden layers. As the training data is fed into the model, it modifies these weights and biases using the errors generated at each step. Hence, our model "learns" the pattern when going through the training data.
Over the last decade, the use of artificial neural networks (ANNs) has increased considerably. People have used ANNs in medical diagnoses, to predict Bitcoin prices, and to create fake Obama videos! With all the buzz about deep learning and artificial neural networks, haven’t you always wanted to create one for yourself? In this book, we’ll create models to carry out two tasks. The first will recognize handwritten digits, while the sceond will carry out facial recognition.
We’ll use the Keras library in this tutorial. Keras is a popular open-source deep learbing framework that provides a Python interface for artificial neural networks, and acts as an interface for the TensorFlow library.
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