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AI, Python, Cognitive Neuroscience

AI, Python, Cognitive Neuroscience

@ai_python_en

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Post #2091 4.4K
Machine Learning w.r.t meditation routine.
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience

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Post #2090 1.03K
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


❇️ @AI_Python_EN
Post #2087 946
Mish is now even supported on YOLO v3 backend. Couldn't have been more elated with how rewarding this project has been. Link to repository -

https://github.com/digantamisra98/Mish

#neuralnetworks #mathematics #algorithms #deeplearning #machinelearning

❇️ @AI_Python_EN
Post #2084 1.34K
Looking for Masters and PhD level students for Paylocity's data science internship program! Students must be in their penultimate year of school, with strong knowledge of machine learning and software engineering. You'll work with Paylocity's incredible talented Product Owners to translate our customers' business needs into data science needs and deliver features that enable next generation HR analytics.

https://2000recruiting.paylocity.com/recruiting/jobs/Details/2767/Paylocity/Data-Scientist-Intern---Summer-2020

❇️ @AI_Python_EN
Post #2080 4.72K
Machine ignoring = underfitting
Machine learning = optimal fitting
Machine memorization = overfitting

#datascience #machinelearning

❇️ @AI_Python_EN
Post #2078 829
Part of the communication challenges between data scientists and the business result from thinking one methodology is going to solve two problems. Illustrative example: The biz asks for a highly predictive churn model (this could be extended to many different use cases, but we're keeping it simple here). In reality, the biz wants to be able to:
1. Accurately identify customers with a high risk of churn so that they can implement some type of corrective measures.
2. They also want recommendations (based on data) that will inform what corrective measures could potentially have the biggest impact on reducing churn. To give the biz what they're expecting, it's possible that you'll need to build two separate models. (one that is highly predictive, the other that is easily interpretable). Bonus, once you've already collected the data, it's not that much incremental effort to build multiple models.
Agree or Disagree? And if you agree, are you already approaching things this way?

❇️ @AI_Python_EN
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