What are the most common mistakes beginners make when starting Deep Learning?

All answers to this question.

The number one mistake is neglecting data quality. Beginners often spend 90% of their time tweaking the model architecture and only 10% on the data. In reality, it should be the opposite. Another huge error is "Data Leakage," where information from your test set accidentally leaks into your training set, giving you fake, high accuracy scores. Lastly, many skip the exploratory data analysis (EDA) phase. If you don't understand the distribution and biases in your raw data, even the most advanced transformer model won't save your project from making poor real-world predictions.

   Answered 2025-01-24 by Betty Campbell

Speaking of accuracy, how do you know if your model is actually learning or if it's just "memorizing" the training data through overfitting?

   Answered 2025-01-26 by Kevin Wright

• Kevin, that's exactly what the validation loss tells you. If your training loss is going down but your validation loss is going up, your model is overfitting (memorizing). You can fix this by using "Dropout" layers, adding more data, or using "Early Stopping" to halt the training before the model starts learning the noise in the data rather than the signal.

     Commented 2025-01-28 by George Russell

A huge mistake is jumping into complex models like Transformers before understanding simple ones. Start small so you can actually debug what goes wrong.

   Answered 2025-01-29 by Sandra Long

• Totally agree. I've seen people try to use BERT for a simple binary classification that a basic Logistic Regression could have solved in two minutes.

     Commented 2025-01-30 by Jason Murphy