Building a Neural Network Class in Python from Scratch

Have you ever wanted to truly understand how deep learning works behind the scenes, rather than just importing black-box libraries? Building a neural network from scratch is the single best way to master the core mathematics and logic of artificial intelligence. This text-based course guides you step-by-step through designing, structuring, and writing a complete neural network class in Python. You will move from foundational mathematical concepts to a fully functional object-oriented implementation, using modern Python best practices like type hints to ensure your code is readable and robust. What you'll learn: - Understand the foundational mathematics of neural networks, including weights, biases, and activation functions - Create a structured Python class to initialize network architecture and layer dimensions - Implement the forward propagation algorithm to pass input data through the network - Code the backpropagation and gradient descent algorithms to train your model from scratch - Apply modern Python features like type hinting to write clean, maintainable machine learning code - Query your trained network to make predictions on new data and evaluate its performance You will begin by learning core machine learning concepts and terminology before diving into hands-on code. Through clear written explanations and structured programming exercises, you will construct each method of your class until you have a working neural network. This course is designed for beginner programmers and aspiring data scientists who want a deep conceptual understanding of neural networks. No prior machine learning experience is required, though basic familiarity with Python is helpful. Start reading today to build your own neural network and demystify deep learning.