Think You’re a Deep Learning Expert? Answer These 5 Questions to Find Out

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

Review the fundamentals, sharpen your skills, and ace that interview with this machine-learning pop quiz!Header created by the author using Canva.

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Deep learning isn’t just about coding or getting a neural network to fit data. Understanding the intricate details and the “why” behind every architectural, training, and deployment decision.

You might be familiar with the standard frameworks or have a few successful models, but can you call yourself an expert?

Below are five challenging questions that test the depth of your understanding. If you can’t answer them confidently, don’t worry; you’re not alone.

Let’s dive into them.

NOTE: Deep-dive questions are at the end of each part, and solutions are listed at the end of the article!

· I. What is the difference between weight initialization schemes like Xavier and He Initialization, and when should you use each?· II. What are the theoretical implications of batch size for the Gradient Descent?· III. Can you explain why the softmax function is preferred in the output layer of multi-class classification models and how it relates to cross-entropy loss?· IV. What is the trade-off between Dropout and Batch Normalization in Deep Networks?· V. How do you address the Vanishing Gradient Problem in Recurrent Neural Networks (RNNs)?· Deep Dive Answers· Do You Have… Read the full blog for free on Medium.

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Published via Towards AI