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11: The Ten Essential Machine Learning Questions

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Contenuto fornito da Sanket Gupta. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Sanket Gupta o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

This episode covers the ten essential machine learning questions. Disclaimer: Baseline answers have been provided in the episode for guidance. For complete accuracy, please refer to textbooks or to courses by Andrew Ng on Coursera.

If this content is useful, please consider buying me a coffee via the link https://anchor.fm/the-data-life-podcast/support

Resources:
1. Machine Learning Course by Andrew Ng: https://www.coursera.org/learn/machine-learning
2. Deep Learning Course by Andrew Ng: https://www.coursera.org/specializations/deep-learning
Questions:
1. What is underfitting and overfitting? How to avoid it?
2. What is the difference between batch, SGD and mini-batch gradient descents? When will you use each?
3. How to choose a machine learning model?
4. How to improve the latency of a machine learning model in production?
5. If your training and cross validation accuracies are high, but testing accuracy is less - how would you debug this?
6. Name 3 hyper-parameters. Why can’t we train them as hyper-parameters, why should only humans set them?
7. Which metric should be used to evaluate a classifier? How do you connect it to business value?
8. What prevents someone to select deep learning model for everything?
9. Say you have to classify a lot of data, but you don’t have labelled training examples. How would you begin to solve the problem? How many training data points are needed?
10. Say you have a perfectly working machine learning model. How do you deploy this in production? How do you check if users will actually like it?

Please leave a review on Apple Podcasts or wherever you listen to this.
Thanks for listening!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/the-data-life-podcast/message Support this podcast: https://podcasters.spotify.com/pod/show/the-data-life-podcast/support

  continue reading

27 episodi

Artwork
iconCondividi
 
Manage episode 243278349 series 2550866
Contenuto fornito da Sanket Gupta. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Sanket Gupta o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

This episode covers the ten essential machine learning questions. Disclaimer: Baseline answers have been provided in the episode for guidance. For complete accuracy, please refer to textbooks or to courses by Andrew Ng on Coursera.

If this content is useful, please consider buying me a coffee via the link https://anchor.fm/the-data-life-podcast/support

Resources:
1. Machine Learning Course by Andrew Ng: https://www.coursera.org/learn/machine-learning
2. Deep Learning Course by Andrew Ng: https://www.coursera.org/specializations/deep-learning
Questions:
1. What is underfitting and overfitting? How to avoid it?
2. What is the difference between batch, SGD and mini-batch gradient descents? When will you use each?
3. How to choose a machine learning model?
4. How to improve the latency of a machine learning model in production?
5. If your training and cross validation accuracies are high, but testing accuracy is less - how would you debug this?
6. Name 3 hyper-parameters. Why can’t we train them as hyper-parameters, why should only humans set them?
7. Which metric should be used to evaluate a classifier? How do you connect it to business value?
8. What prevents someone to select deep learning model for everything?
9. Say you have to classify a lot of data, but you don’t have labelled training examples. How would you begin to solve the problem? How many training data points are needed?
10. Say you have a perfectly working machine learning model. How do you deploy this in production? How do you check if users will actually like it?

Please leave a review on Apple Podcasts or wherever you listen to this.
Thanks for listening!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/the-data-life-podcast/message Support this podcast: https://podcasters.spotify.com/pod/show/the-data-life-podcast/support

  continue reading

27 episodi

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