Foundations of Neural Networks and Deep Learning in Python

Deep learning is the engine behind modern artificial intelligence, but mastering it requires understanding the core mechanics under the hood. This text-based course guides you through the fundamental mathematics and programming concepts needed to build neural networks from scratch. You will transition from writing standard Python scripts to designing, training, and troubleshooting your own deep learning models, gaining a clear mental model of how artificial brains learn. What you'll learn: Understand the fundamental terminology of neural networks, including activation functions, weights, biases, and backpropagation; Build a fully functioning neural network from scratch using Python and NumPy to comprehend the underlying math; Apply modern Python development practices, including virtual environments and type hints, to write clean, maintainable machine learning code; Implement gradient descent and optimization algorithms to train models efficiently; Configure and evaluate deep learning models for classification and regression tasks; Explore modern deep learning architectures, including an introduction to convolutional networks and the basics of transformers. The course starts with essential mathematical foundations and basic terminology before moving step-by-step through building, training, and optimizing networks with clear, written code explanations. It is designed for Python programmers who want to enter the field of artificial intelligence, with no prior machine learning experience required. Start reading today to unlock the potential of deep learning in your Python projects.