Category: Deep Learning
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?
What is an Activation Function? An activation function is a critical component in neural networks. It determines a neuron’s output after the neuron processes its inputs by computing a weighted sum. The activation function decides whether the neuron should be activated or not, introducing nonlinearity to the model. This nonlinearity enables the model to learn…
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 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 MoreWhat is perceptron?
The perceptron is a type of artificial neural network (ANN) that is designed to recognize patterns in data. It can be used to identify objects, classify images, and detect changes in the environment. The perceptron was invented by Frank Rosenblatt in 1957 while he was working at Cornell Aeronautical Laboratory as part of a research…
Read MoreWhat is L1 and L2 regularization in Deep Learning?
In deep learning, L1 and L2 regularization are regularization techniques used to penalize the model’s weights during the training process. This penalty discourages the model from assigning excessive importance to certain features, thereby reducing the risk of overfitting. L1 Regularization L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value…
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