Welcome to Week 1 of the Fundamentals of Deep Learning course. In this week, you will be introduced to the core concepts of deep learning and set clear expectations for what you will learn throughout the course. We will begin by understanding what deep learning is and how it fits within the broader fields of artificial intelligence and machine learning. You will explore how data is processed inside a neuron, gaining insight into the building blocks of neural networks. The week then focuses on how deep learning models learn, covering key concepts such as gradient descent, forward propagation, and backward propagation. Through demonstrations, you will see how a neuron is trained and how activation functions enable neural networks to learn complex, non-linear patterns. By the end of this week, you will have a strong foundational understanding of deep learning fundamentals, including how neural networks are structured, how learning and optimization take place, and the role of activation functions in training deep learning models.

Welcome to Week 2 of the Fundamentals of Deep Learning course. This week focuses on the practical application of deep learning techniques for real-world problems, with an emphasis on model training, evaluation, and modern neural network architectures. You will begin by working on multi-class classification using the MNIST dataset, where you will train and evaluate a deep learning model and understand how performance is measured. The week then introduces Convolutional Neural Networks (CNNs), explaining how they are designed to effectively learn from image data. You will also explore transfer learning techniques, learning how pre-trained models can be reused and adapted for new tasks. Through hands-on demonstrations, you will implement transfer learning on an image dataset and evaluate model performance. By the end of this week, you will be able to train and evaluate deep learning models for classification tasks, understand CNN-based architectures, and apply transfer learning to efficiently solve image-based deep learning problems.