Deep Learning Basics: Train Your First Keras Sequential Model

Deep learning can feel overwhelming with its complex math and vast frameworks, but starting with a structured, step-by-step approach makes it highly accessible. This text-based course guides you through the foundational concepts of neural networks, helping you write clean, modern code to train your first model. You will transition from understanding basic theory to confidently structuring data and evaluating how neural networks learn. What you will learn: Understand the core concepts of layers, activation functions, and sequential models; Prepare and format sample datasets specifically for training neural networks; Build and compile a fully connected sequential model using Keras; Train your model on sample data and monitor key training metrics; Analyze how model accuracy changes and identify signs of overfitting; Practice writing clean, modern Python code with proper type hints for machine learning workflows. The course begins with essential deep learning terminology and foundational definitions before moving into hands-on code construction and model evaluation. This course is designed specifically for beginners with basic Python knowledge who want a clear, conceptual, and practical introduction to neural networks without any complex prerequisites. Read, practice, and start your journey into deep learning today.