What is the difference between batch gradient descent and stochastic gradient descent?
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
- Definition: Batch gradient descent is an optimization algorithm that updates the model's parameters using the average gradient of the entire training dataset. Stochastic gradient descent, on the other hand, updates the parameters using the gradient of a single training example at a time
- Computational Efficiency: Batch gradient descent requires computing the gradients for all training examples in each iteration, which can be computationally expensive for large datasets. Stochastic gradient descent, on the other hand, only requires computing the gradient for one training example at a time, making it more computationally efficient
- Convergence: Batch gradient descent usually converges to the global minimum of the cost function, given a sufficiently small learning rate. Stochastic gradient descent, however, converges to a local minimum or an area close to it, due to the noisy nature of updating the parameters using a single training example
- Noise & Variance: Batch gradient descent provides a less noisy estimate of the gradient since it considers the entire training dataset. Stochastic gradient descent, on the other hand, has a higher variance in the estimated gradient due to the randomness introduced by using a single training example
- Learning Rate: In batch gradient descent, the learning rate can be set to a larger value since the gradient is averaged over the entire dataset. In stochastic gradient descent, a smaller learning rate is typically used to ensure convergence, as the gradient estimate from a single example can be noisy
- Batch Size: Batch gradient descent uses the entire training dataset as a batch, while stochastic gradient descent uses a single training example as a batch. However, mini-batch gradient descent is a compromise between the two, where a small subset of the training dataset is used as a batch
- Parallelization: Batch gradient descent is inherently sequential since it requires computing the gradients for all training examples. Stochastic gradient descent, on the other hand, can be parallelized as the gradients for different training examples can be computed independently
- Memory Usage: Batch gradient descent requires storing the entire training dataset in memory to compute the gradients. Stochastic gradient descent, on the other hand, only requires storing a single training example in memory at a time, making it more memory-efficient
- Applications: Batch gradient descent is commonly used when the training dataset can fit into memory and computational efficiency is not a major concern. Stochastic gradient descent is often preferred for large-scale datasets or online learning scenarios where computational efficiency and memory usage are important factors
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Technical knowledge: Understanding of machine learning optimization algorithms
- Problem-solving skills: Ability to choose the appropriate optimization algorithm for different scenarios
- Critical thinking: Analyzing and comparing different optimization techniques
- Understanding of trade-offs: Awareness of the advantages and disadvantages of each algorithm
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Not being able to explain the basic concepts of batch gradient descent and stochastic gradient descent
- Confusion: Mixing up the concepts or not clearly distinguishing between batch and stochastic gradient descent
- Incomplete answer: Failing to mention important differences between the two algorithms, such as the use of entire dataset vs. single data points, or the impact on convergence speed
- Lack of practical knowledge: Inability to discuss the advantages and disadvantages of each algorithm in real-world scenarios
- Inability to apply knowledge: Not being able to explain how to choose between batch and stochastic gradient descent based on specific problem requirements or dataset characteristics