Building Neural Networks: Neurons and Layers in TensorFlow and Keras

Neural networks are the backbone of modern artificial intelligence, but understanding how they actually process information can feel overwhelming. By breaking down deep learning to its simplest components—individual neurons and layers—you can demystify how machines learn from data. This text-only course guides you through the foundational concepts of neural networks, demonstrating how biological inspiration translates into digital mathematical operations. You will transition from understanding basic terminology to building, configuring, and training your own neural network architectures. What you'll learn: Understand the core biology-inspired concepts behind artificial neurons, weights, biases, and activation functions; Configure dense layers and construct multi-layer network architectures using the Keras sequential API; Apply loss functions and optimizers to train models that learn from input data; Implement modern regularization techniques like dropout to prevent overfitting and improve model generalization; Evaluate model performance using validation datasets and interpret training metrics. You will start with the absolute fundamentals of single-neuron math before moving step-by-step into multi-layered architectures, learning how data flows forward and how errors are corrected. Through clear written explanations and structured code snippets, you will gain a practical mental model of deep learning. This course is designed for beginners who want a conceptual and practical introduction to neural networks without needing prior experience in advanced calculus or deep learning. Start reading today to unlock the fundamental building blocks of modern machine learning.