One-on-one discussion on Gradient Descent

Usually, the learners from our classes schedule 1-on-1 discussions with the mentors to clarify their doubts. So, thought of sharing the video of one of these 1-on-1 discussions that one of our CloudxLab learner – Leo – had with Sandeep last week.

Below are the questions from the same discussion.

You can go through the detailed discussion which happened around these questions, in the attached video below.

One-on-one discussion with Sandeep on Gradient Descent
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Top Machine Learning Interview Questions for 2019 (Part-1)

 

These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.

For hiring machine learning engineers or data scientists, the typical process has multiple rounds.

  1. A basic screening round – The objective is to check the minimum fitness in this round.
  2. Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
  3. ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
  4. Bar Raiser / Hiring Manager  – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.

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How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

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