Try to create a video upon back propagation and what are the basics of algorithms while we need to make

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Welcome to backpropagation! Neural networks are powerful learning systems that can recognize patterns, make predictions, and solve complex problems. But how do they actually learn? The secret lies in adjusting their internal parameters called weights and biases. Backpropagation is the fundamental algorithm that makes this learning possible by efficiently calculating exactly how to adjust these parameters to minimize errors. Before we understand backpropagation, let's see how a neural network makes predictions. In the forward pass, data flows from input to output. Here, input 2 is multiplied by weight 3 to get 6, then multiplied by weight 0.8 to get output 5. But our target was 3, so we have an error. The loss function measures this error - in this case, the squared difference gives us a loss of 2. Our goal is to adjust the weights to minimize this loss. To minimize the loss, we use gradient descent - an optimization algorithm that adjusts weights in the direction that reduces loss most quickly. The gradient tells us which direction to move. But calculating gradients for deep networks is complex because weights in early layers affect the loss through many intermediate calculations. This is where the chain rule comes in. The chain rule allows us to break down complex derivatives into simpler parts, multiplying gradients from each layer. Backpropagation applies this principle to efficiently compute gradients for all weights in the network. Now let's see backpropagation in action. First, we perform a forward pass to calculate the output and loss. Then comes the backward pass - the heart of backpropagation. We start by calculating the error at the output layer. This error signal then propagates backward through the network. At each layer, we compute the gradient of the loss with respect to that layer's weights using the chain rule. Finally, we use these gradients to update the weights via gradient descent. This entire process repeats many times, gradually improving the network's performance. To summarize, backpropagation is built upon several fundamental algorithms working together. The neural network architecture provides the structure. Forward propagation computes predictions and loss. The chain rule from calculus enables efficient gradient calculation. Gradient descent uses these gradients to optimize weights. These components form a continuous training cycle: forward pass, loss calculation, backpropagation, and weight updates. This elegant combination makes backpropagation the cornerstone algorithm that powers modern deep learning, enabling networks to learn complex patterns from data.