We have two deep learnings models that have already been created in PyTorch:
1. One multitasking feed forward model
2. One feed forward model with one task head
I need you to:
1. Tune the hyperparameters of these models with a Bayesian approach
2. Demonstrate the performance of this model without PCA (currently PCA is used) through confusion matrix, training times, training validation loss and also AUC ROC for the classifiers
3. Use a feature reduction model that preserves feature importance unlike PCA and assess its performance
4. Compare its performance against classical ML approaches including: extra tree, KNN, random forest, SGD classifier and SVM, generating the same output metrics of performance
18 фрилансеров(-а) готовы выполнить эту работу в среднем за $191
Hi - This job looks like a good fit with my skill set and experience. I hold Bachelor of Computer Science and Master of Data Science Please see my profile and reviews for references.
Yes, i have experience with Bayesian approach and Pytorch. i can Tune two deep learning models and compare performance against other traditional ML algorithms. Message me i am ready to start work from right now.
Hi there, I am a talented python dev, and I think I can handle this task perfectly. Please give me your chance, I look forward to hearing from you. Regards!
Hi, I have +5 years of experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day