30 points by joefit 6 months ago flag hide 22 comments
john_doe 6 months ago next
Interesting project! I'm curious, what ML algorithms are you using to personalize the fitness plans?
fitness_app_dev 6 months ago next
We're using a combination of clustering algorithms for grouping users with similar characteristics and a supervised learning model trained to predict the best exercise type based on the user's goal and profile.
jane_doe 6 months ago prev next
How accurate have your predictions been?
fitness_app_dev 6 months ago next
Our predictions have been quite accurate, with a success rate of around 85%. We plan to improve this by adding more features and retraining our models.
will_smith 6 months ago prev next
That's really cool! How did you gather data for training your ML models?
fitness_app_dev 6 months ago next
We had a team of fitness experts create a dataset with various exercises and user profiles to serve as the basis for our training set. We used a portion of this data for testing and tweaking our algorithms before using them in the app.
garbage_collector 6 months ago prev next
I'm concerned about user privacy. How do you address data privacy in your app?
fitness_app_dev 6 months ago next
User privacy is of utmost importance to us. We only collect data that is essential for our algorithms to function. We also ensure that our database is encrypted, and that our users have full control over their data.
somebody_here 6 months ago prev next
Can users input their own exercises? Or is it strictly limited to a set list?
fitness_app_dev 6 months ago next
Users are able to input their own exercises and routines. Our algorithms can analyze these exercises and suggest potential modifications to maximize the benefits to the user.
mr_robot 6 months ago prev next
Have you considered using federated learning to improve privacy while still gathering user data?
fitness_app_dev 6 months ago next
Federated learning is an excellent idea, and we're exploring the possibility of incorporating it into our system. Thank you for bringing it up.
always_learning 6 months ago prev next
Could you share some resources or papers you've found useful for designing your system?
fitness_app_dev 6 months ago next
Certainly! I recommend starting with 'Machine Learning for Fitness and Sports' and 'A Survey of Machine Learning Techniques in Recommender Systems'. Both are excellent resources that have guided us in building our app.
newbie_developer 6 months ago prev next
Wow, your app sounds fantastic! Would you consider open-sourcing part of your project for those learning ML to contribute and learn from?
fitness_app_dev 6 months ago next
We appreciate your kind words, and we are actually considering open-sourcing parts of the project in the near future. Stay tuned for more updates!
skeptic 6 months ago prev next
Sounds great, but how easy is it for a user to actually follow the plan your app generates?
fitness_app_dev 6 months ago next
Our user interface has a clean, intuitive design and includes animations for each exercise, making it easy for users to follow their personalized plan.
private_person 6 months ago prev next
Has your app been reviewed by professionals in the field? How do you validate your algorithms' suggestions?
fitness_app_dev 6 months ago next
Yes, our app has been reviewed and vetted by various fitness professionals. They have examined our algorithms' output and confirmed that the recommendations are safe and effective for their intended purpose.
machine_guru 6 months ago prev next
What type of compute and hardware are you running to power these ML processes?
fitness_app_dev 6 months ago next
We use a combination of cloud resources from AWS and GCP, depending on the workload and scale of the model inferencing. Most of our ML models are containerized and run on Kubernetes clusters.