Computer Vision Engineer
Computer Vision Engineer Interview Questions and Answers for Freshers
Q1. How would you approach the problem of training a model to detect this plastic bottle?
I would approach the problem by collecting a dataset of images containing plastic bottles, preprocessing the images, selecting a suitable model architecture, training the model, and evaluating its performance.
Collect a dataset of images containing plastic bottles and label them accordingly
Preprocess the images by resizing, normalizing, and augmenting them to improve model performance
Select a suitable model architecture such as Convolutional Neural Network (CNN) for image clas...read more
Q2. What is the difference between CNN and RNN
CNN is used for image recognition while RNN is used for sequence data like text or speech.
CNN is Convolutional Neural Network, used for image recognition tasks.
RNN is Recurrent Neural Network, used for sequence data like text or speech.
CNN has convolutional layers for feature extraction, while RNN has recurrent connections for sequential data processing.
CNN is good at capturing spatial dependencies in data, while RNN is good at capturing temporal dependencies.
Example: CNN can...read more
Q3. Why are activation functions used
Activation functions are used to introduce non-linearity into neural networks, allowing them to learn complex patterns and relationships.
Activation functions help neural networks to learn complex patterns and relationships by introducing non-linearity.
They help in controlling the output of a neuron, ensuring that it falls within a desired range.
Common activation functions include ReLU, Sigmoid, Tanh, and Leaky ReLU.
Without activation functions, neural networks would simply be...read more
Q4. Is Logloss function differentiable
Yes, Logloss function is differentiable.
Logloss function is differentiable as it is a smooth and continuous function.
The derivative of Logloss function can be calculated using calculus.
Differentiability is important for optimization algorithms like gradient descent to converge smoothly.
Example: The derivative of Logloss function for binary classification is (predicted probability - actual label).
Q5. What is learning rate
Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
Learning rate determines the size of the steps taken during optimization.
A high learning rate can cause the model to overshoot the optimal weights, while a low learning rate can result in slow convergence.
Common learning rate values are 0.1, 0.01, 0.001, etc.
Learning rate can be adjusted during training using techniques like learning rate sche...read more
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