Assembling Neural Networks from Perceptrons for Handwritten Digit Recognition

Deep learning powers modern artificial intelligence, but truly mastering it requires understanding the fundamental building blocks from the ground up. This written course takes you behind the scenes of neural networks, guiding you through the transition from simple single-layer perceptrons to multi-layer architectures. You will learn how to structure, train, and optimize these models using foundational mathematics and matrix operations.\n\nBy completing this course, you will transform your conceptual understanding into practical engineering skills, gaining the ability to design and evaluate neural networks from scratch.\n\nWhat you'll learn:\n- Understand the core mechanics of a single perceptron, including weights, biases, and activation functions\n- Assemble multiple perceptrons into dense, layered architectures to capture complex data patterns\n- Apply matrix multiplication and vectorized operations to process data efficiently\n- Practice implementing forward propagation and evaluating model accuracy on the MNIST dataset\n- Learn modern optimization techniques and loss functions to refine network performance\n- Configure neural network training workflows, from data preprocessing to final evaluation\n\nThis course begins with essential definitions and the history of the perceptron before moving into multi-layer configurations and mathematical implementations. You will read structured explanations, analyze clear code snippets, and work through conceptual exercises to solidify your knowledge.\n\nThis course is designed for beginners in machine learning and data science who want to understand how deep learning works under the hood. No advanced background in neural networks is required, though basic familiarity with Python and algebra is helpful.\n\nStart reading today to build a rock-solid foundation in neural network architecture.