Explain the core concepts of deep learning and artificial neural networks
Implement and train feedforward networks using backpropagation and gradient descent
Design and apply convolutional neural networks for image recognition tasks
Build recurrent neural networks and LSTMs for sequential data analysis
Compare and choose among Keras, TensorFlow, and PyTorch for deep learning projects
Apply techniques like dropout, normalization, and data augmentation to improve model performance
