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Poisson distribution is a probability distribution that expresses the likelihood of a given number of events occurring in a fixed interval of time or space.
Describes the number of events that occur in a fixed interval of time or space
Events are independent of each other
Average rate of occurrence is constant
Examples: number of emails received in an hour, number of customers arriving at a store in a day
Bias-variance trade off is the balance between underfitting and overfitting in machine learning models.
Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.
Variance refers to the model's sensitivity to fluctuations in the training data, leading to overfitting.
Finding the right balance between bias and variance is crucial for creating a model that generalizes well to unseen ...
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Optimizers are algorithms used to adjust the parameters of a model to minimize the error between predicted and actual values.
Optimizers are used in machine learning to improve the accuracy of models.
They work by adjusting the weights and biases of a model during training.
Common optimizers include Gradient Descent, Adam, and RMSprop.
The choice of optimizer depends on the type of problem and the characteristics of the da...
RELU is an activation function used in neural networks to introduce non-linearity.
RELU stands for Rectified Linear Unit.
It is a simple function that returns the input if it is positive, and 0 otherwise.
It is commonly used in deep learning models due to its simplicity and effectiveness.
Other activation functions include sigmoid, tanh, and softmax.
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I applied via Recruitment Consulltant and was interviewed before Jun 2023. There were 5 interview rounds.
Homoscedasticity refers to the assumption that the variance of errors is constant across all levels of the independent variable.
Homoscedasticity is a key assumption in linear regression analysis.
It indicates that the residuals (errors) have constant variance.
If the residuals exhibit a pattern where the spread of points increases or decreases as the predicted values increase, it violates the assumption of homoscedastici...
Biasing is the error due to overly simplistic assumptions in the learning algorithm. Overfitting is when a model is too complex and fits the training data too closely, leading to poor generalization. Underfitting is when a model is too simple to capture the underlying structure of the data.
Biasing occurs when a model has high error on both training and test data due to oversimplified assumptions.
Overfitting happens whe...
Deep learning neural networks are a type of artificial neural network with multiple layers, used for complex pattern recognition.
Deep learning neural networks consist of multiple layers of interconnected nodes, allowing for more complex patterns to be learned.
They are capable of automatically learning features from data, eliminating the need for manual feature engineering.
Examples include Convolutional Neural Networks ...
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