<jupyter_start><jupyter_text>Using a Neural Network to Fit the Data The following is my attempt to provide some intutition around one of the reasons deep learning is useful, namely the lack of a need feature engineering. The intuition was gained by reading this book and several other works.   Artificial Neuronsloosely inspired by the working of the human brain, aritifical neurons are at its core a linear transofrmation followed by a non linear *activation function*.  The Neuron: $o = tanh(w*x+b)$$x$ *is the input*$w$ *and* $b$ *are the learned parameters*$w*x+b$ is the linear transformation $tanh$ is the non linear activation function<jupyter_code><jupyter_output><empty_output><jupyter_text>The non linear activation function serves two purposes, which are important for approximating complex functions: 1. The non linearity means the slope of the output with respect to the parameter( a weight) can change depending on the value of the output. 2. The bounds provided by many of the activation functions, ensures that the output remains within a range. For eg: the output of a tanh funciton remains between -1 and +1The output is sensitive to the weights only in a region<jupyter_code><jupyter_output><empty_output>
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