Foundations of Artificial Neural Networks through Manual Calculations

To truly understand how deep learning works, you must first grasp the underlying mechanics of artificial neural networks. Relying solely on high-level libraries can leave you with gaps in your understanding when debugging or optimizing model architectures. This text-only course guides you through the fundamental mathematics and structural design of neural networks. By performing step-by-step manual calculations, you will build a solid intuitive grasp of how data flows through a network, how errors are calculated, and how weights are updated during training. This foundational knowledge prepares you to confidently transition to advanced deep learning concepts. What you'll learn: - Understand the biological inspiration behind artificial neurons and the history of perceptrons. - Analyze different network topologies and activation functions like Sigmoid, ReLU, and Tanh. - Calculate feedforward propagation and loss functions manually to trace data flow. - Perform backpropagation by hand using the chain rule to update weights and biases. - Explore modern optimization techniques such as Stochastic Gradient Descent and Adam. - Design basic network architectures on paper to solve simple classification and regression tasks. We begin with foundational definitions, comparing biological neurons to artificial models, and exploring basic network architectures. You will then progress through the mathematical mechanics of training, working through detailed, written step-by-step calculation exercises that demystify the backpropagation algorithm. This course is designed for absolute beginners, aspiring data scientists, and students who want a deep, mathematical understanding of neural networks before writing code. No prior experience with deep learning libraries is required, though basic algebra is helpful. Start reading today to build a bulletproof foundation in neural network mechanics.