Fundamentals of Deep Learning

Fundamentals of Deep Learning is a structured course designed for developers, data professionals, and AI enthusiasts who want to build a strong foundation in neural networks and modern deep learning techniques. This course focuses on core deep learning principles, including how artificial neurons work, forward and backward propagation, gradient descent optimization, activation functions, multi-class classification, Convolutional Neural Networks (CNNs), and transfer learning. Through a progressive and practical learning path, you will gain hands-on experience training neural networks, evaluating model performance, and applying deep learning techniques to real-world image classification problems. The course bridges theory and implementation, helping you understand not just how models work, but why they work. Whether you are beginning your journey in artificial intelligence or preparing for advanced machine learning and cloud-based AI roles, this course equips you with the conceptual clarity and practical skills required to confidently build and evaluate deep learning models. This course includes approximately 3:30–4:00 hours of video lectures, combining foundational theory with step-by-step demonstrations. It is divided into focused modules that progressively develop your understanding of neural network architecture and applied deep learning techniques. To reinforce learning, each module includes quizzes and in-video practice questions that test conceptual understanding and practical application. 📘 Module 1: Foundations of Deep Learning and Neural Networks 🧠Module 2: Deep Learning Models, Computer Vision, and Transfer Learning

• Fundamentals of Deep Learning is a structured course designed for developers, data professionals,
• AI enthusiasts who want to build a strong foundation in neural networks
• Modern deep learning techniques
• Through a progressive
• Practical learning path, you will gain hands-on experience training neural networks, evaluating model performance,
• Applying deep learning techniques to real-world image classification problems
• The course bridges theory
• Implementation, helping you understand not just how models work, but why they work

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work