If you want to become a deep learning specialist and bag a job in this field, you will not only need to keep up with the pace of this ever-changing field and its new developments, but also practice answers to some of the frequently asked interview questions in this field. We bring you some help with these Q&As to let you ace your deep learning job interview with confidence.
The popular applications of deep learning
What are some of the popular applications of deep learning?
Today, deep learning is used in a wide range of fields, the most popular ones among which are:
- Computer Vision
- Sentiment Analysis
- Virtual Assistants
- Image Recognition and Processing
- News Aggregation
- Automatic Text Generation
- Natural Language Processing
- Object Detection
- Robotics
CNN (Convolutional Neural Network)
What’s a CNN (Convolutional Neural Network)?
In the domain of deep learning, a CNN refers to a category of deep neural nets that are most commonly applied for analyzing visual imagery. In other words, a CNN takes an input image and allocates importance (learnable biases and weights) to the different objects/aspects in the image to become capable of distinguishing one from the other.
Central to the CNN is the convolutional layer, which executes a process called a “convolution”. This is a linear operation where a set of weights is multiplied with the input, similar to a traditional neural network. Since the technique was planned for 2D inputs, the multiplication is executed between a set of input data and a 2D group of weights, called a kernel or a filter.
Apart from the input and output layers, CNNs typically have multiple pairs of convolutional and pooling layers, which are followed by several consecutive convolutional layers (also called fully connected layers), and lastly, a regression layer or a softmax layer to produce the desired outputs.
Deep learning platform and a deep learning library
What’s a deep learning platform and a deep learning library?
A deep learning platform offers a set of tools together with an interface for constructing custom deep nets. As a user, you will get to choose from a selection of deep nets on a deep learning platform along with the ability to combine data from various sources, manipulate it, and manage models via the user interface. If you need to train a net with a massive dataset to improve performance, you can use some of these platforms designed for the task.
A deep learning library refers to a group of modules and functions that you can call through your own programs to carry out certain tasks. By using deep net libraries, you can enjoy a high degree of flexibility with hyperparameter configuration and net selection.
Name some of the deep learning frameworks or tools that you have used?
I have worked with the following deep learning frameworks and tools (make sure to pick the ones from the list below that you have experience with):
- Keras
- TensorFlow
- PyTorch
- Caffe2
Wrapping up
While these questions may not be that difficult to answer, you need to make sure your answers convince the interviewer of your clear idea about the fundamental concepts of deep learning.
