Foundations of Neural Networks and Deep Learning with Python

Deep learning is the driving force behind modern artificial intelligence, powering everything from natural language processing to computer vision. Understanding how neural networks function is the first step toward building intelligent systems and unlocking new career paths in AI. This text-based course guides you through the core concepts of deep learning, enabling you to write clean, modern Python code to build and train your own neural networks from scratch. What you'll learn: - Understand the mathematical foundations of neural networks, including activation functions, backpropagation, and gradient descent. - Build and train multi-layer perceptrons using modern Python libraries and structured type hints. - Configure deep learning models using popular frameworks like PyTorch for basic classification tasks. - Apply optimization techniques and regularization to prevent overfitting and improve model performance. - Explore modern deep learning concepts, including an introduction to transformer architectures and modern model evaluation workflows. You will start with foundational AI terminology and basic mathematical concepts before gradually moving into practical network architecture, training loops, and modern optimization strategies. Each module offers clear written explanations, code walkthroughs, and conceptual exercises to reinforce your learning. This course is designed for beginners who want to understand the mechanics of deep learning. A basic familiarity with Python is helpful, but no prior experience with machine learning or advanced mathematics is required. Start reading today to master the core principles of neural networks and begin your journey into deep learning.