Neural Networks: Theory Explained! + Practical Guide!

In this video, we’ll cover the core theory of neural networks—what neurons, layers, weights and biases do, how activation functions like ReLU and softmax work, and the magic of forward and backpropagation. Then we’ll dive into a practical build, using TensorFlow to load the MNIST dataset, define a simple 3 dense‑layer model, compile with the right loss and optimizer, and train it to recognize handwritten digits. Finally, watch a live demo of digit classification in action, pick up quick tips for boosting accuracy, and grab the full source code in the pinned comment. Don’t forget to 👍, subscribe for more AI & ML guides, and leave your questions in the comments!