Uncrumpling complex data representation (paper fold) to neat representation (straightened paper) It is same as uncrumpling the paper ball to a neat looking paper as shown in the below diagram. Deep learning can be understood as uncrumpling a highly folded data manifolds into neat representation of data. The following analogy is taken from the book, Deep Learning with Python by François Chollet. Similarly, a rotation of a 2D vector by an angle \(\theta\) can be achieved via a dot product with a 2 × 2 Matrix.īased on above, it can be comprehended that a neural network can be seen as a very complex geometric transformation in a high-dimensional space, implemented via a long series of simple arithmetic operations. For instance, arithmetic operation on two vectors result in another vector which can be visualized in the following manner. These computations result in geometric transformations of input data. The neurons perform computation on input data and results in an output based on the activation function. The core to simple (single layer or MLP) neural network or deep neural network (2 or more hidden layers) is the computation units called neurons laid out in layers and connected with neurons of another layers. Deep Neural Network representing Deep Learning How does the Deep Neural Network work? As there are linkages between the computation unit aka neurons across different layer, so the name “neural network”. Make a note of multiple hidden layers and blue circles representing computation unit called as neuron. ![]() Here is a diagram representing the deep neural network trained with inputs to create predictions (outputs). What is Deep Neural Network?ĭeep neural network is artificial neural network with 2 or more hidden layers. A neural network having one input layer, one hidden layer and one output layer is called as multi-layer perceptron (MLP) network. When all the neurons across different layers are connected with each other, the neural network is also called as fully-connected neural network.Ī neural network having just one neuron can be called as a sungle-layer neural network. ![]() Each neuron is associated with what is called an activation function which decides on the output of the neuron. In the above equation, the \(w_n\) represents the weight and \(x_n\) represents the corresponding input. Neuron as a computation unit can be expressed as a weighted sum of inputs and looks like the following: Before going ahead, lets understand what is artificial neural network? What is Artificial Neural Network?Īrtificial neural network is a bunch of computation units called neurons laid out in one or more layers while the neurons being connected with each other. ![]() And, the model created with neural network having 2 or more hidden layers apart from input and output layer is said to be based on deep learning. Neural network with more than 1 hidden layer (or 2 or more hidden layers) can be termed as deep neural network. Deep learning represents deep neural network.
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