Deep Learning interview questions Part -1
1 – What is data normalization? What’s the need for it? Data normalization is a process of transforming data from one format to another in order to improve the quality of the data and make it more usable for analysis. In this process data is organized and formatted in such a way that it’s easier…
Read MoreWhat is Tanh activation function?
The Tanh Activation function is a scaled and shifted version of the hyperbolic tangent function, a mathematical function frequently encountered in trigonometry and calculus. The Tanh function squashes input values within the range of -1 to 1, making it a useful choice for activation functions in neural networks. Defining the Tanh Function Mathematically The mathematical…
Read MoreWhat is PReLU and ELU activation function?
PReLU(Parametric ReLU) – PReLU is vital to the success of deep learning. It solves the problem with activation functions like sigmoid, where gradients would often vanish. This approach is finding more and more success in deep learning environments. But, we can still improve upon ReLU. Leaky ReLU was introduced, which does not zero out the…
Read MoreDifference between Leaky ReLU and ReLU activation function?
Leaky ReLU is a type of activation function that helps to prevent the function from becoming saturated at 0. It has a small slope instead of the standard ReLU which has an infinite slope Leaky ReLU is a modification of the ReLU activation function. It has the same form as the ReLU, but it will…
Read MoreWhat is ReLU and Sigmoid activation function?
The activation function is a nonlinear function that takes in the weighted sum and produces the output. They are used to provide a more simplified model of neuron behavior which can be used as an input to deep neural networks. There are many different activation functions that can be used, including sigmoid, hyperbolic tangent, logistic,…
Read MoreWhat is Vanishing and exploding gradient descent?
Vanishing and exploding gradient descent is a type of optimization algorithm used in deep learning. Vanishing Gradient Vanishing Gradient occurs when the gradient is smaller than expected. It causes the earlier layers to start degrading before the later ones do, causing a decrease in the overall learning rate of that subset of layers. The weights…
Read MoreWhat is forward and backward propagation in Deep Learning?
Forward propagation is a process in which the network’s weights are updated according to the input, output and gradient of the neural network. In order to update the weights, we need to find the input and output values. The input value is found by taking the difference between the current hidden-state value and that of…
Read MoreWhat is multilayer perceptron?
A multi-layer perceptron is a type of artificial neural network. It has one or more hidden layers between the input and output layers, each of which can be thought of as a series of processing units connected to each other in a hierarchical tree structure. The input layer nodes are connected to the hidden layer…
Read MoreDifference between machine learning and machine reasoning?
Machine Learning is a subset of artificial intelligence, which is a type of statistical learning. It provides computer programs with the ability to automatically learn from data without being explicitly programmed where to look for patterns. Machine Learning algorithms do not need to be explicitly programmed where to look for patterns in order to find…
Read MoreDifference between perceptron and neuron?
Perceptrons are a type of artificial neural network that can be used for classification and regression. They are supervised learning algorithms, meaning they need labeled input data in order to learn. how to map inputs to outputs. What independent variables do perceptrons need? Perceptrons require at least one input and one output. What are the…
Read More