What is the purpose of the activation function in a neural network?
Theme: Machine Learning Algorithms Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Machine Learning Algorithms with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Activation Function: The activation function is a mathematical function that introduces non-linearity to the neural network
- Non-linearity: The activation function allows the neural network to learn and model complex relationships between inputs and outputs
- Thresholding: The activation function helps in thresholding the output of a neuron, determining whether it should be activated or not
- Gradient Calculation: The activation function is used to calculate the gradients during the backpropagation process, which is crucial for updating the weights and biases of the neural network
- Normalization: The activation function helps in normalizing the output of a neuron, ensuring that the values fall within a specific range
- Differentiation: The activation function should be differentiable to enable efficient optimization algorithms like gradient descent to be applied
- Common Activation Functions: Some commonly used activation functions include sigmoid, tanh, ReLU, and softmax
- Sigmoid Function: The sigmoid function is often used in the output layer of a binary classification problem, as it maps the output to a probability between 0 and 1
- ReLU Function: The rectified linear unit (ReLU) function is widely used in hidden layers, as it helps in mitigating the vanishing gradient problem and speeds up training
- Tanh Function: The hyperbolic tangent (tanh) function is similar to the sigmoid function but maps the output to a range between -1 and 1, making it suitable for classification problems
- Softmax Function: The softmax function is commonly used in the output layer of multi-class classification problems, as it normalizes the outputs into a probability distribution
- Choosing Activation Function: The choice of activation function depends on the problem at hand, network architecture, and the desired properties of the neural network
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Knowledge of neural networks: Understanding the role and importance of activation functions in neural networks
- Problem-solving skills: Ability to select appropriate activation functions for different tasks and network architectures
- Critical thinking: Analyzing the impact of different activation functions on network performance and optimization
- Technical expertise: Demonstrating familiarity with various activation functions and their mathematical properties
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Providing a vague or incorrect explanation of the purpose of activation function
- Inability to explain different activation functions: Not being able to discuss and differentiate between popular activation functions like sigmoid, ReLU, and tanh
- Limited knowledge of neural networks: Failing to connect the activation function's role in neural networks and its impact on model performance
- Inability to discuss non-linear transformations: Neglecting to mention that activation functions introduce non-linearities, enabling neural networks to learn complex patterns
- Lack of awareness of activation function selection: Not mentioning the importance of selecting appropriate activation functions based on the problem domain and network architecture