Building Neural Networks From Scratch: Math, Algorithms, and Python

Do you want to move beyond simply calling library functions and truly understand how modern artificial intelligence models work? Mastering the mathematical and algorithmic foundations of neural networks is essential for anyone serious about deep learning. This course provides a foundational, hands-on path to building neural networks from first principles. By the end, you will possess a deep theoretical understanding of common network architectures, optimization methods, and the practical ability to implement them efficiently using Python. What you'll learn: * Understand the core linear algebra and calculus behind feedforward propagation and backpropagation. * Build foundational network components (layers, activation functions, loss functions) using only standard Python libraries. * Apply optimization algorithms like Gradient Descent and its variants to train models effectively. * Practice modern Python techniques, including vectorization, for efficient numerical computation. * Analyze the internal mechanics of popular deep learning frameworks like TensorFlow and PyTorch. The content begins by establishing the necessary mathematical prerequisites and introducing fundamental concepts. We then progress to step-by-step implementation of basic neural networks, followed by practical exercises focused on training and optimization. This course is designed exclusively for beginners in AI and deep learning who have basic Python knowledge but no prior experience with neural network theory or implementation. No advanced mathematical background is required to start. Start your journey toward becoming a skilled AI practitioner today.