Learning

Deep Learning and Neural Networks: From Foundations to Deployment

Linear Algebra Foundations for Deep Learning → Deploying a Trained Neural Network

A structured path from the mathematical and programming foundations of deep learning through building, training, and deploying neural networks. Learners progress from linear algebra and calculus essentials, through Python and automatic differentiation, to core architectures (MLPs, CNNs, RNNs, Transformers), training methodology, and practical deployment. Each node is a distinct, teachable unit, ordered by dependency so that no step assumes knowledge that has not yet been built. The destination is the ability to design, train, evaluate, and deploy a neural network for a real task.