How Neural Networks Work: Understanding Feedforward, Backpropagation, and the Training Loop
How do neural networks work?
In this video, we’ll break down the entire process, from how data moves through a network (feedforward) to how it learns and improves through backpropagation and training loops. Whether you’re a beginner in AI or a data engineer looking to understand the fundamentals, this video will give you a clear roadmap. What you’ll learn:
The structure of a neural network: neurons, layers, and connections.
The feedforward process: how inputs flow to generate predictions.
Backpropagation and training: optimizing weights and biases to minimize loss.
The difference between gradients, backprop, and gradient descent.
By the end of this video, you’ll understand the full training process, including feedforward, loss calculation, backpropagation and the role of optimizers.
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