How to deliver on Machine Learning projects

A guide to the ML Engineering Loop.

By Emmanuel Ameisen and Adam Coates:
https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0

#ArtificialIntelligence #BigData #DataScience #DeepLearning #MachineLearning

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Research Guide: Advanced Loss Functions for Machine Learning Models

http://bit.ly/36HBefu

#DataScience #MachineLearning #ArtificialIntelligence

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Visualizing an AI model’s blind spots

http://bit.ly/2CosFZn

#DataScience #MachineLearning #ArtificialIntelligence

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Grid search vs randomized search?
💡 What are the pros and cons of grid search? Pros: • Grid search is great when you need to fine-tune hyperparameters over a small search space automatically. • For example, if you have 100 different datasets that you expect to be similar (e.g. solving the same problem repeatedly with different populations), you can use grid search to automatically fine-tune the hyperparameters for each model. Cons: • Grid search is computationally expensive and inefficient, often searching over parameter space that has very little chance of being useful, resulting it being extremely slow. It's especially slow if you need to search a large space since it's complexity increases exponentially as more hyperparameters are optimized.
💡 What are the pros and cons of randomized search? Pros: • Randomized search does a good job finding near-optimal hyperparameters over a very large search space relatively quickly and doesn't suffer from the same exponential scaling problem as grid search. Cons: • Randomized search does not fine-tune the results as much as grid search does since it typically does not test every possible combination of parameters.
#datascience
👉 Free training -> http://bit.ly/dsdj-webinar


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4 Traits, qualities that a data scientist must seek ...

1) Technical bar: Data science teams work everyday in SQL, specifically in Postgres, and expect candidates to know Python/some fluency in some sort of statistical language. Also, someone who is really comfortable with querying really large datasets.

2) Communication: we’re in roles where a lot of our day-to-day is spent getting great insights or building models and communicating results of that to stakeholders, whether that’s product managers, marketing folks or finance. It’s super key that data science candidates have good communication skills.

3) Grit, tenacity and willingness to solve hard problems: Things that DS teams solve are generally hard problems. My hope is that anyone who joins the data science team is excited about hard problems and bumping against hard challenges.

4) Passion for the arts and passion for the mission: This is not the most important but great to have.
#datascience

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Microsoft: Actor critic method bests greedy exploration in #reinforcementlearning

http://bit.ly/2sfxt17

#DataScience #MachineLearning #ArtificialIntelligence

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In #datascience, you must understand context. There are times at work where looking at the data alone didn't help me from solving the problem.

It doesn't matter if your domain is in marketing, healthcare, product, etc... You need to understand the context first before diving into the data. Without background information about how the data was generated, it becomes really difficult to make accurate assumptions on what your data will show.

Taking the time to understand the context will not only benefit you in your analysis, but you may even help your colleagues tackle the problem better.

When you are informed about the data and problem, you increase your value because now you're in a position to communicate and identify other potential problems.

So do this:
On your next project, take the time to not just do EDA, but also document your understanding of the context behind the data.

This good practice will definitely help you in your career and is a valuable skill you can bring to any team.
Context first, data second.

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