Google Interview Hacks

Describe back-propagation. We can calculate the error of the network only at the output units. The hidden units represent latent variables; we cannot observe their true values in the training data and thus, we have nothing to compute their error against. In order to update their weights, we must propagate the network's errors backwards through its layers. We will begin with Output1. Its error is equal to the difference between the true and predicted outputs, multiplied by the partial derivative of the unit's activation. Continue this process all the way to the input variables and then forward propagate the updated weights through the network.

What are the two main types of artificial neural networks? 1) Feedforward neural networks are the most common type of neural net, and are defined by their directed acyclic graphs. Signals only travel in one direction—towards the output layer—in feedforward neural networks 2) Feedback neural networks, or recurrent neural networks, do contain cycles. The feedback cycles can represent an internal state for the network that can cause the network's behavior to change over time based on its input.

Describe the Multilayer perceptron (MLP) The multilayer perceptron (MLP) is the one of the most commonly used artificial neural networks. The name is a slight misnomer; a multilayer perceptron is not a single perceptron with multiple layers, but rather multiple layers of artificial neurons that can be perceptrons. The layers of the MLP form a directed, acyclic graph. Generally, each layer is fully connected to the subsequent layer; the output of each artificial neuron in a layer is an input to every artificial neuron in the next layer towards the output.

What are the three components to an Artificial Neural Network? 1) The model's architecture, or topology, which describes the layers of neurons and structure of the connections between them. 2) The activation function used by the artificial neurons. 3) The third component is the learning algorithm that finds the optimal values of the weights.

What is kernalization? Answer: Projects linearly inseparable data to a higher dimensional space in which it is linearly separable.

What is an epoch? Answer: Each pass through the training instances

What is the perceptrons update rule? Answer: w(t + 1) = w(t) + alpha (d_j - y_j(t))x_i_j, for all features 0 <= i <= n