What is Deep Learning?
If you’re a job seeker interested in data science, it’s important to understand what deep learning is and to practice some deep learning interview questions.
Deep learning is a subfield of machine learning that involves building and training artificial neural networks with multiple layers to recognize patterns in data. It has revolutionized many industries, including healthcare, finance, and transportation, by enabling computers to perform tasks that were once only possible for humans.
As a job seeker, having a solid understanding of deep learning and its applications can make you a valuable candidate for positions in data science, artificial intelligence, and machine learning. Many companies are seeking professionals who can develop and implement deep learning models to solve complex problems and create innovative solutions.
To build your skills in deep learning, you can take online courses, participate in online communities, attend industry events, and work on personal projects. Showcasing your expertise in deep learning through your projects and contributions can also help you stand out to potential employers.
Recruiters and hiring managers use skill assessments, interviews, and cultural-fit assessments to evaluate your qualifications and suitability for a specific role. If you are preparing for a job and want more job individual interview questions use AboutSkills (about-skills.com). This platform simulates the technical environment of a role and assesses your understanding of deep learning concepts, among other critical skills. By performing well in these assessments, you demonstrate your competence in deep learning, which could increase your chances of securing your dream job. AboutSkills job simulations provide a great opportunity to showcase your expertise and stand out in a competitive job market.
Use the questions to prepare for a technical interview
If you’re preparing for a technical interview, it’s a good idea to review the topics and questions that may be asked. One helpful resource is a list of 7 questions and answers compiled together with experts from Skillfill (skillfill.ai) to help jobseekers to prepare for a technical job interview. The reasoning behind each answer can also help you better master the interview. In case you want more questions with answers read the article from skillfill.ai.
Question 1 Which is the most appropriate activation function in the output layer of a deep learning model used for binary classification?
| Option A) | ReLu | Option B) | Sigmoid |
| Option C) | Softmax | Option D) | Don’t know |
Correct answer: (B)
Explanation: If you ask this question to your junior Machine Learning engineer candidates, you are evaluating their knowledge about deep neural networks.
The sigmoid function maps any input to the range of 0 and 1, which represents the probability of the positive class. The output of the sigmoid function can then be thresholded to obtain the final binary prediction.
Question 2 Imagine you are training your linear regression model via gradient descent. In the graph below, the loss function is plotted as a number of iterations for various learning rates. What could be a feasible relation of the learning rates (lr)?
|
Option A) |
lr_blue < lr_black < lr_orange < lr_green | Option B) | lr_blue > lr_green > lr_orange > lr_black |
| Option C) | lr_orange = lr_green = lr_blue = lr_black | Option D) |
Can’t say without knowing the data set. |
Correct answer: (B)
Explanation: By asking this to your candidate, you are assessing their knowledge regarding model training, model improvement, and linear regression.
Finding the right learning rate needs trial and error as it is very dependent on your problem and data set. Very high learning rates most probable doesn’t let your model predict anything properly as it is too coarse to come closer to the optimum hence the loss could start spiking up after a couple of iterations (blue). Very low learning rates usually go in the direction of a minimum but very slowly which makes them very computationally costly (black). The optimum lies in the middle, where the optimum is quickly approached and a saturation is found after not too many iterations (orange). The green example would be of a learning rate still too high to approach the minimum, hence staying stuck at a similar loss.
Question 3 When training a deep neural network, what action can be applied to reduce overfitting? [multiple solutions possible]
| OptionA) | Addition of regularization. | Option B) | Reduction of training data. |
| Option C) | Reduction of network complexity. | Option D) | Utilization of data augmentation. |
Correct answer: (A), (C), and (D)
Explanation: Evaluate the knowledge about deep neural networks development. Bellow you can see why A), B) and C) are correct:
A) Regularization techniques, such as L1 and L2 regularization, add penalties to the loss function for large weights, which discourages the model from learning overly complex representations.
C) Reducing the number of neurons or layers in the network can help prevent overfitting by limiting the capacity of the model to memorize the training data.
D) Data augmentation involves creating new training examples by applying random transformations to the original training data, increasing the size of the training set and reducing overfitting.
Question 4 What happens if you replace all ReLu functions with linear activation functions in the architecture below?
|
Option A) |
The network will not be able to model non-linear functions. | Option B) | The network will perform slightly better. |
| Option C) | The network will take slightly longer in training time. | Option D) |
Can’t say. |
Correct answer: (A)
Explanation: In general, replacing ReLU activation functions with linear activation functions can result in a decrease in the performance and capacity of the network, and may make the training process slower and more difficult. ReLU activation functions add non-linearity to the network, allowing it to model complex relationships in the data. Linear activation functions do not have this property, so replacing ReLUs with linear activation functions may result in a decreased ability of the network to model complex relationships in the data. Performance will be worse because of this fact. The training time is not affected by the change.
Question 5 What is a potential problem when using a sigmoid activation function compared to e.g. using a ReLU activation function when training a neural network?
| OptionA) | The sigmoid function is computationally more elaborate compared to the ReLU function. | Option B) | Large gradients get saturated in the sigmoid function. |
| Option C) | The sigmoid function is not non-linear. | Option D) | Can’t say |
Correct answer: (B)
Explanation: Vanishing Gradients: The sigmoid activation function produces output values that are limited to a small range close to 0 or 1, which can result in very small gradients during backpropagation. This can cause the gradients to disappear or “vanish”, making it difficult for the network to learn from the data. This is known as the vanishing gradient problem. The sigmoid function is non-linear, and computationally similarly elaborate as the ReLu function.
Question 6 In the following picture, the red box marks a region of significant discontinuity. What type of discontinuity are we talking about?
| Option A) | Depth | Option B) | Surface color |
| Option C) | Illumination | Option D) | Distinct |
Correct answer: (A)
Explanation: Depth discontinuity refers to a sharp change in the depth or distance of objects in a scene. In computer vision and image processing, depth discontinuity is often used to detect boundaries between foreground and background objects, or to distinguish between objects at different distances from the camera.
Question 7 What statement is correct about the optimization algorithm AdaGrad applied commonly in training Machine Learning algorithms?
| Option A) | AdaGrad applies first order differentiation. | Option B) | AdaGrad applies second order differentiation. |
| Option C) | AdaGrad applies dynamic order differentiation choosing the degree based on the problem at hand. | Option D) | Can’t say |
Correct answer: (A)
Explanation: AdaGrad applies first order differentiation because it uses the gradient information to adjust the learning rates of individual parameters. The gradient is a first-order derivative of the loss function with respect to the model parameters, and it represents the direction of maximum increase of the loss function.
