Intro to Neural Networks

Biological Inspiration

Artificial Neurons

Layers & Activation

Training a Network

Backpropagation Algorithm

Loss Functions and Optimizers

Overfitting and Underfitting

Regularization Techniques

Advanced CNN Architectures and Applications

Recurrent Neural Networks (RNNs) for Sequence Data

Validation and Hyperparameter Tuning

Building a Multilayer Perceptron (MLP)

Introduction to Convolutional Neural Networks (CNNs)

Model Evaluation and Practical Considerations

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)

Introduction to Transformers and Attention Mechanisms

Transfer Learning and Fine-tuning Pre-trained Models

Data Augmentation for Robust Model Training

Generative Adversarial Networks (GANs)

Model Interpretability and Explainable AI (XAI)