🔥 Build an artificial neural network in Java (10% off with code NEURAL): https://www.caveofprogramming.com/products/courses/create-a-neural-network-in-java 👉 Python and Machine Learning, Fast-Track for Programmers: https://www.caveofprogramming.com/products/courses/python-for-java-developers 👉 Python and Machine Learning for Complete Beginners: https://www.caveofprogramming.com/products/courses/python-machine-learning-beginners Discover the fundamentals of neural networks with a clear, step‑by‑step walkthrough of how they work, including the backpropagation algorithm in detail. 00:00 Introduction 01:06 Human Neurons 02:30 Artificial Neurons 04:36 Layered Architecture 06:10 Table of Variables 06:30 Points to Understand 11:03 Wrapping in Tensors 13:27 Matrix Multiplication 19:39 Softmax 22:43 Loss and Categorical Cross-Entropy 28:24 Gradient Descent 31:27 Calculus 38:28 Backpropagation 50:12 Training the Hidden Layer 55:56 Backpropagating through ReLU 57:31 Batch Training 58:30 Epochs and Supervised Learning 59:03 Relevant Courses 59:57 Summing Up This video explains how inputs flow through layers, how weighted sums and activations are computed, how each component contributes to the final output; and how rates of change are propagated backwards to modify the weights in a neural network, enabling the network to learn patterns in data. Perfect for beginners and anyone wanting a solid, intuitive understanding of how ANNs work. You’ll see the math, the intuition, and the structure behind ANNs — all presented in a simple, visual, and easy‑to‑follow way.