1. Weeds out candidates that aren't ready for the job.
2. It significantly cuts down the time we spend talking synchronously.
One problem: It requires extra time from the candidate before the phone interview.
10 machine learning questions you should be able to answer.
I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.
Thread: Let's look at 10 of these questions.
1. Weeds out candidates that aren't ready for the job.
2. It significantly cuts down the time we spend talking synchronously.
One problem: It requires extra time from the candidate before the phone interview.
• 249 applicants
• 13 good applications
That's only 5%.
This sucks.
• 44 applicants
• 21 good applications
That's 47%.
This is much better.
Sooner or later, I'll ask these questions, and the candidate will have to answer them.
I'd rather give them space and the opportunity to think about their answers without any pressure.
Those who can't or don't want to answer won't apply. That's fine.
They will fool the questionnaire, not the interview that will come later.
Remember:
• The goal of the questionnaire and the resume is just to decide who moves to an interview.
I want to allow candidates to think creatively and elaborate on their answers.
Preferred: A video response with the answers. Fallback is written answers if the candidate is not comfortable with video.
These cover basic principles that we want to ensure candidates understand.
Every candidate receives the same questions.
Let's go over them:
2. Walk us through the process you follow to select the best machine learning algorithm to solve a problem.
3. Explain the trade-off between bias and variance.
5. How do you handle imbalanced datasets in a classification problem?
6. Explain how you would approach reducing overfitting on a model.
8. What would you expect to see as you vary the batch size when training a neural network?
10. What are the advantages of a Convolutional Neural Network over a fully connected network for image classification?
Follow along for a good bunch of practical tips and epic stories about my experience with machine learning.
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Free machine learning education.
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
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Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
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Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
Here are 6 of these programs. Pick one and get started!
↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
https://t.co/cKzpDOBVl1
Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
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Another common #question:
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