Tune two deep learning models and compare performance against other traditional ML algorithms -- 2
$30-250 CAD
Paid on delivery
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
Project ID: #31791332
About the project
7 freelancers are bidding on average $161 for this job
We will do your Python work I am writing this proposal in order to work for you in Software and Web Development. We are highly trained professional developers seeking to freelance and earn online. Having a flair in pr More
Hello, I'm a machine learning engineer who currently working. Most of my time I'm coding for model optimization, monitoring and tuning. I'd be glad if we can talk about the details. Best regards.
Hi, I am a researcher at the Indian Institute of Science Bangalore. I have 6+ years of experience in machine learning and deep learning. I am familiar with PyTorch/TensorFlow/sklearn. I understand the baysian optim More
20 years development .net oracle java wpf wcf banking applications graduated from computer engineering department
