Foundations of Neural Networks and Deep Learning

Deep learning is driving the modern artificial intelligence revolution, but truly mastering it requires understanding how neural networks function under the hood. This text-based course guides you through the foundational mathematics and programming structures that power deep learning models. You will transition from a beginner to a confident practitioner capable of designing, vectorizing, and training fully connected deep networks from scratch. What you'll learn: Understand the fundamental mathematical concepts behind neural networks, including forward propagation, backpropagation, and activation functions; Build and train a fully connected deep neural network using clean Python code; Apply vectorization techniques using modern numerical libraries to optimize training speed and efficiency; Configure network architectures, tuning key parameters such as learning rates and layer depth; Implement modern programming practices, including type hints and modular structures, to keep your deep learning code maintainable. The curriculum begins with essential terminology and the basic mechanics of single neurons before progressing step-by-step to multi-layer architectures and vectorized implementations. This course is designed for aspiring AI developers, data scientists, and programmers who want a solid, code-first introduction to deep learning without needing prior experience in neural networks. Start building your deep learning foundation today through clear, structured written tutorials.