This course builds a practical foundation in deep learning by distinguishing it from traditional machine learning. You’ll explore how neural networks function, how backpropagation and optimization enable learning, and how to evaluate models using real-world metrics. Core concepts like overfitting and regularization are introduced early to help you build models that perform reliably. You’ll then dive into essential architectures powering modern AI. Learn how CNNs handle image data, how RNNs, GRUs, and LSTMs process sequences, and how autoencoders and transformers support dimensionality reduction and attention-based modeling. Each module focuses on when and why to use these models effectively. In the final phase, you’ll move from theory to deployment. Using TensorFlow, PyTorch, and Google Colab, you’ll apply data augmentation, normalization, and mixed precision training. You'll also explore model explainability, transfer learning, versioning, and deployment—preparing you to deliver production-ready deep learning solutions.