Deep Learning and Neural Networks

This exam validates your skills in applying deep learning to images, text and complex patterns. You demonstrate an understanding of the principles behind neural networks and show that you can build, train and optimize models in Python on your own.

You are tested on topics such as:

• Neural network fundamentals, activation functions and layers
• Building and training models in TensorFlow/Keras or PyTorch
• Overfitting and regularization: dropout, early stopping, batch norm
• Image classification with Convolutional Neural Networks (CNN)
• Transfer Learning and fine-tuning of pretrained models
• Text classification with RNN/LSTM, embeddings or pretrained models
• Hyperparameter tuning and using TensorBoard for evaluation
• Interpretability and responsible use (SHAP, LIME, production considerations)