help me understand backpropgation by explaning how it works and how to calucalte forward pass and gradient componenets of a first and second layer of a basic neural network please
Demystifying Backpropagation: A Simple Explanation
💻Technology
Featured Chapters
Understanding Backpropagation: The Forward Pass
00:00:05 - 00:00:08
Backpropagation: The Backward Pass
00:01:18 - 00:01:22
Sources
What is backpropagation in neural networks?
https://serokell.io/blog/understand...
Backpropagation in Neural Network - GeeksforGeeks
https://www.geeksforgeeks.org/backp...
Just a moment...
https://www.datacamp.com/tutorial/m...
Background Music: "SPACE" prod. Metzmusic
Transcript
Welcome to this in-depth (less) video on backpropagation! In this chapter, we'll explore the forward pass, the first step in this crucial neural network training technique.
Let's visualize a simple neural network. We have inputs, hidden layers processing those inputs, and finally, an output. The forward pass is simply the flow of information through this network.
Forward Pass: Step 1: Data Input. The process begins by feeding input data into the network's input layer.
Think of it like providing the network with the ingredients for its calculations.
Forward Pass: Step 2: Weighted Sum. Each input is multiplied by its corresponding weight, and these weighted sums are passed to the neurons in the next layer.
This is represented mathematically as a weighted sum. Each connection has a weight, influencing the signal's strength.
Forward Pass: Step 3: Activation Function. The neurons apply an activation function to the weighted inputs. This introduces non-linearity, crucial for complex pattern recognition.
ReLU, sigmoid, and softmax are common examples. They introduce non-linearity, allowing the network to learn complex relationships.
Forward Pass: Step 4: Output. The output from the last hidden layer is passed to the output layer, where another activation function is applied to generate the final output.
Now that we understand the forward pass, let's dive into the backward pass, where the magic of learning happens.
Backward Pass: Step 1: Error Calculation. The network's output is compared to the ground truth, and the difference, or error, is calculated.
This error tells us how far off our prediction was.
Backward Pass: Step 2: Gradient Calculation. The error gradient is propagated backward through the network, using the chain rule of calculus.
The chain rule helps us determine how much each weight contributed to the overall error.
Backward Pass: Step 3: Weight Update. Using gradient descent, we adjust the weights to reduce the error. This iterative process refines the network's ability to make accurate predictions.
We repeat this process, gradually improving the network's accuracy.
Let's visualize a simple neural network. We have inputs, hidden layers processing those inputs, and finally, an output. The forward pass is simply the flow of information through this network.
Forward Pass: Step 1: Data Input. The process begins by feeding input data into the network's input layer.
Think of it like providing the network with the ingredients for its calculations.
Forward Pass: Step 2: Weighted Sum. Each input is multiplied by its corresponding weight, and these weighted sums are passed to the neurons in the next layer.
This is represented mathematically as a weighted sum. Each connection has a weight, influencing the signal's strength.
Forward Pass: Step 3: Activation Function. The neurons apply an activation function to the weighted inputs. This introduces non-linearity, crucial for complex pattern recognition.
ReLU, sigmoid, and softmax are common examples. They introduce non-linearity, allowing the network to learn complex relationships.
Forward Pass: Step 4: Output. The output from the last hidden layer is passed to the output layer, where another activation function is applied to generate the final output.
Now that we understand the forward pass, let's dive into the backward pass, where the magic of learning happens.
Backward Pass: Step 1: Error Calculation. The network's output is compared to the ground truth, and the difference, or error, is calculated.
This error tells us how far off our prediction was.
Backward Pass: Step 2: Gradient Calculation. The error gradient is propagated backward through the network, using the chain rule of calculus.
The chain rule helps us determine how much each weight contributed to the overall error.
Backward Pass: Step 3: Weight Update. Using gradient descent, we adjust the weights to reduce the error. This iterative process refines the network's ability to make accurate predictions.
We repeat this process, gradually improving the network's accuracy.