Define Bias and Threshold?What is learning rate paramenter? Bias Neural Network is conceptually based on actual neuron of brain. Neurons are the basic units of a large neural network. A single neuron passes single forward based on input provided. In Neural network, some inputs are provided to an artificial neuron, and with each input a weight is associated. Weight increases the steepness of activation function. This means weight decide how fast the activation function will trigger whereas bias is used to delay the triggering of the activation function. For a typical neuron, if the inputs are x1, x2, and x3, then the synaptic weights to be applied to them are denoted as w1, w2, and w3. Equation is y = f(x) = Σxiwi where i is 1 to the number of inputs. The weight shows the effectiveness of a particular input. More the weight of input, more it will have impact on network. On the other hand Bias is like the intercept added in a linear equation. It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. Therefore Bias is a constant which helps the model in a way that it can fit best for the given data. The processing done by a neuron is thus denoted as : output = sum (weights * inputs) + bias Threshold A threshold transfer function is sometimes used to quantify the output of a neuron in the output layer. Feed-forward networks include Perceptron (linear and non-linear) and Radial Basis Function networks. Feed-forward networks are often used in data mining. A feed-back network (e.g., recurrent neural network or RNN) has feed-back paths meaning they can have signals traveling in both directions using loops. All possible connections between neurons are allowed. Since loops are present in this type of network, it becomes a non-linear dynamic system which changes continuously until it reaches a state of equilibrium. Feed-back networks are often used in associative memories and optimization problems where the network looks for the best arrangement of interconnected factors. Learning rate Parameter In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Since it influences to what extent newly acquired information overrides old information, it metaphorically represents the speed at which a machine learning model "learns". In the adaptive control literature, the learning rate is commonly referred to as gain. In setting a learning rate, there is a trade-off between the rate of convergence and overshooting. While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that direction.A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum. In order to achieve faster convergence, prevent oscillations and getting stuck in undesirable local minima the learning rate is often varied during training either in accordance to a learning rate schedule or by using an adaptive learning rate. The learning rate and its adjustments may also differ per parameter, in which case it is a diagonal matrix that can be interpreted as an approximation to the inverse of the Hessian matrix in Newton's method. The learning rate is related to the step length determined by inexact line search in quasi-Newton methods and related optimization algorithms.