Foundations of Deep Learning: Gradient Descent and Backpropagation

Deep learning powers today's most advanced artificial intelligence, but truly mastering it requires looking beneath the surface of pre-built libraries. This text-based course guides you through the essential mathematics and mechanics that make neural networks learn. You will transition from simply running code to deeply understanding the inner workings of AI models. By studying clear mathematical explanations and step-by-step code implementations, you will build a robust theoretical foundation that makes you a more versatile and capable developer. What you'll learn: - Understand the fundamental components of neural networks, including weights, biases, and activation functions. - Implement gradient descent from scratch to optimize model parameters and minimize training loss. - Demystify backpropagation by tracing the mathematical chain rule through network layers. - Write clean Python code to build and train a basic neural network without relying on heavy external frameworks. - Explore how modern libraries like TensorFlow and PyTorch automate these core mathematical operations. - Apply key evaluation metrics to assess your model's training progress and prevent common optimization errors. The course begins with essential terminology and foundational mathematical definitions before moving into optimization theory and practical code walkthroughs. You will progress systematically from basic formulas to structured network modeling. This course is designed for beginners in AI and machine learning who have a basic familiarity with Python, requiring no prior background in advanced calculus. Start reading today to unlock the core mechanics of deep learning and build your AI expertise from the ground up.