Deep Learning interview questions are integral to tech interviews for machine learning engineers, data scientists, and other similar roles. You must have a solid understanding of concepts like NLP, computer vision, and neural networks to crack Deep Learning interview questions.
With several tech companies looking for professionals who can use machine learning and deep learning to build models that mimic human behavior, the demand for engineers with experience in Deep Learning is growing.
To grab your dream role, you must be prepared for Deep Learning coding interview questions. In this article, we've covered some frequently asked Deep Learning interview questions to help you prepare for your tech interview.
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Following are some deep learning coding interview questions and answers to get you started.
This is an important Deep Learning coding interview question. You must know the following types of activation functions:
Each node in a recurrent neural network has an additional loop. This makes it different from artificial neural network propagation. This loop incorporates a temporal component into the network. The main advantage of recurrent neural networks is that they allow for sequential data information. This is usually impossible with a generic artificial neural network.
If you are well-versed in Deep Learning, you can answer these types of Deep Learning interview questions with ease.
A deep learning model may be solely built on linear regression. However, the problem should be represented by a linear equation, which does not boost the machine learning model's predictive capacity due to the addition of nodes. Hence, building a deep learning model solely on linear regression creates no spectacular results.
This is one of the important topics asked in Deep Learning interview questions.
A computational graph is a series of operations performed to take inputs and arrange them as nodes in a graph. It is a way of implementing mathematical calculations into a graph. This way, it will help in parallel processing and provide high performance in terms of computational capability.
This is a commonly asked in Deep Learning interview question. You must have a sound understanding of what autoencoders are to answer this.
Autoencoders are used worldwide. Some of the popular usages of autoencoders are:
You must know there are four types of autoencoders. They are:
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To excel at ML or data science interviews, you must have profound knowledge of natural language processing (NLP) in Deep Learning. Following are a few questions that you must practice to nail your Deep Learning interview at FAANG and tier-1 tech companies.
These types of Deep Learning interview questions test your fundamental knowledge of the subject.
When developing NLP tools to work with exceptional data, it's beneficial to attain a canonical representation of textual content. This is known as textual normalization. Textual normalization captures different kinds of variations into one representation.
When you employ machine learning methods to complete your modeling, you need to input pre-processed text into an NLP algorithm. This set of strategies used for this process is known as feature engineering or feature extraction. The main purpose of feature extraction is to convert the text's qualities into a numeric vector that NLP algorithms can understand. This stage is known as text representation.
TF-IDF is known as Term-Frequency-Inverse Document Frequency. It helps you get the importance of a particular word relative to other words in the corpus. It converts words into vectors and adds semantic information, resulting in weighted unusual words. These words can be utilized in various NLP applications. Moreover, it's a common scoring metric in information retrieval and summarization.
A part-of-speed (POS) tagger reads the text in a language and assigns speed parts to each word, such as noun, verb, adverb, and others. POS taggers employ an algorithm to label terms in text bodies. These labels create various complex categories with tags like "noun plural" or other complicated labels.
This is one of the most asked Deep Learning interview questions. The differences between NLP and NLU are:
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If you are applying for a role of a Computer Vision Engineer in any top company, you must practice the following Deep Learning computer vision interview questions to uplevel your preparation:
Neural network's earlier layers detect simple features of an image (for example, edges or corners). As you go deeper, the features become increasingly complex, detecting patterns and shapes in the neural network. The later layers can detect intricate patterns, such as complete objects.
You can use padding to address the issue of filter or kernel extracting information from the edge pixels less compared to the central pixel. Padding is the addition of one or more rows or columns of pixels along the boundary of the image.
It forms the new pixels of the picture. Therefore, it results in insufficient extraction of information from the original edge pixels. It also prevents the shrinking of an image due to the convolution operations.
You should know that for an nxn image with an fxf filter, padding p, and stride length s, resultant image's size after convolution has the shape n + 2p - fs + 1 x n + 2p - fs + 1. Therefore, per the data provided, the resulting size of the image will be (((5 + 2 * 1 - 3) / 2) + 1) x (((5 + 2 * 1 - 3) / 2) + 1)= 3 x 3.
The convolution operation is not possible for such dimensions of an RGB image. The third dimension (number of channels) should be the same to achieve convolution. However, if the 10x10x3 image is convolved in a 3x3x3 filter, the dimensions of the resultant image will be 4x4.
The pooling layer contains hyperparameters describing the filter size and the stride length. These parameters are set and work as a fixed computation. Hence, no parameters are to be learned in the pooling layers.
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Here are a few sample Deep Learning interview questions that you must prepare to enhance your preparation for your next tech interview:
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Q1. Are Deep Learning interview questions tough?
Deep Learning interview questions are easier if you're well-acquainted with the subject. If you are a beginner, preparing these Deep Learning interview questions may take more time. But once you get a command of the fundamentals, preparing for the Deep Learning interview will be easier than thought.
Q2. How do I crack a deep learning interview?
To crack deep learning interview questions, you should be well-versed with the following concepts: neural network basics, multilayer perceptrons, convolutional neural networks, system design, embeddings, recurrent neural networks, long-short term memory, and transformers.
Q3. What is deep learning short answer?
Deep learning is a method of teaching logical functioning to computers and devices. It mimics the way humans gain knowledge.
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