Can We Learn the Language of Proteins?
#DataScience #MachineLearning #ArtificialIntelligence
http://bit.ly/33jCof6
❇️ @AI_Python_EN
#DataScience #MachineLearning #ArtificialIntelligence
http://bit.ly/33jCof6
❇️ @AI_Python_EN
Research Guide: Advanced Loss Functions for Machine Learning Models
http://bit.ly/36HBefu
#DataScience #MachineLearning #ArtificialIntelligence
❇️ @AI_Python_EN
http://bit.ly/36HBefu
#DataScience #MachineLearning #ArtificialIntelligence
❇️ @AI_Python_EN
Visualizing an AI model’s blind spots
http://bit.ly/2CosFZn
#DataScience #MachineLearning #ArtificialIntelligence
❇️ @AI_PYTHON_EN
http://bit.ly/2CosFZn
#DataScience #MachineLearning #ArtificialIntelligence
❇️ @AI_PYTHON_EN
Machine ignoring = underfitting
Machine learning = optimal fitting
Machine memorization = overfitting
#datascience #machinelearning
❇️ @AI_Python_EN
Machine learning = optimal fitting
Machine memorization = overfitting
#datascience #machinelearning
❇️ @AI_Python_EN
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
💡 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
Machine Learning w.r.t meditation routine.
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience
❇️ @AI_Python_EN
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience
❇️ @AI_Python_EN
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
❇️ @AI_Python_EN
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
❇️ @AI_Python_EN