Structure of a Neural Network (Layers, Neurons, Weights)

Forward Propagation

Activation Functions (ReLU, Sigmoid, Tanh)

Loss Functions & Cost

Backpropagation

Training a Neural Network: Epochs, Batch Size

Convolutional Neural Networks (CNNs)

CNN in Action: Image Classification

Recurrent Neural Networks (RNNs)

RNN in Action: Predicting Text

Transformers (Intro only) & Modern Architectures

Collecting and Preparing Data

Overfitting, Underfitting, and Regularization

Optimization Algorithms (SGD, Adam)

Evaluation Metrics (Accuracy, Precision, Recall)

Fine-Tuning & Transfer Learning

Real-Life Deep Learning Use Cases

Introduction to Tools: TensorFlow, Keras, PyTorch

Building Your First DL App (No-code or beginner Colab notebook)

Deep Learning Career Paths

Future Trends: Generative AI, Large Language Models