The Only Interview Prep Course You Need for Deep Learning

Dive into the 50 most popular deep-learning questions to get you ready for your interview.



The Only Interview Prep Course You Need for Deep Learning
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Suppose you are preparing for a data science, machine learning engineer, AI engineer, or a research scientist job. In that case, you should look for great resources to help you ace your interview. 

Deep learning is becoming more and more popular, as it forms the foundations of topics such as large language models, and generative AI, as well as combining a lot of different concepts. This is why this interview prep course is probably one of the best things I have seen in a while. 

Not only will you get a great foundational and experience knowledge of deep learning, but you will also enhance your data science and machine learning skills. Even if you are not preparing for any interview but you are on a learning journey - I would recommend this interview prep course!

 

Deep Learning Interview Course

 

This course consists of 2 parts. The first part, the video will go through the top 50 questions with corresponding answers. In the second part, the video will go through the remaining 50 questions. 

100 questions altogether. That's 7.5 hours altogether!



 

Basic Interview Questions

 

You will start with the basic questions of deep learning, the concepts of neural networks, the architecture of neural networks, activation functions and gradient descent. These are the first 10 questions, therefore you will go through these quite quickly. 

 

Intermediate Interview Questions

 

In the next 20 questions, you will dive a bit deeper and be able to define how backpropagation is different from gradient descent and cross-entropy. From there, you will dive a little deeper and test your skills in areas such as Stochastic Gradient Descent and Hessian and how they can be used to speed up the training process. 

 

Expert Interview Questions

 

The last 20 questions will test your knowledge with topics such as Adam and its use in neural networks, what is layer normalization, residual connections, and how to solve exploding gradients. You will also dive into learning more about dropout and what it is, how it prevents overfitting, the curse of dimensionality, and more. 

 

Happy Learning

 

We hope that this course has helped you become more confident for your upcoming interview or your learning process in general. Going over the top interview questions will help you understand what is important knowledge and what interviewers deem as important skills and knowledge. 

If you know of any other good resources, please share them in the comments for the community!
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.