Menghitung Manual Neural Network Menggunakan Excel

Summary

This tutorial guides viewers through building a simple neural network (perceptron) with two input features and one output. It covers initializing weights, applying a threshold activation function, calculating errors, and updating weights iteratively using a learning rate. The process is demonstrated step-by-step in a spreadsheet, showing how the model gradually minimizes error across multiple iterations until convergence. The video also includes testing the trained model on new data to predict outcomes. Overall, it provides a hands-on, practical introduction to supervised learning and perceptron modeling, helping learners understand the mechanics behind neural network training and prediction.

Takeaways

• 😀 The video tutorial explains how to implement a simple neural network using a perceptron with 2 input features and 1 output.
• 😀 The dataset consists of 7 training examples with features X1, X2, and their corresponding labels Y.
• 😀 Initial weights are set manually (W1 = 0.3, W2 = -0.4) and a learning rate of 0.04 is used.
• 😀 The activation function used is a threshold function, outputting 1 if the sum is greater than 0.5 and 0 otherwise.
• 😀 For each data point, the prediction is calculated, error is computed, and weights are updated accordingly.
• 😀 Weight updates follow the formula: W_new = W_old + (learning_rate * error * feature_value).
• 😀 The tutorial demonstrates iterating through the training data multiple times until the error is minimized or reaches zero.
• 😀 Intermediate steps, including summation of weighted inputs, predicted outputs, and error calculation, are shown for clarity.
• 😀 After training, the model can predict new, unseen data using the final updated weights without modifying them.
• 😀 Experimenting with parameters like learning rate and number of iterations affects convergence and accuracy.
• 😀 The process emphasizes understanding supervised learning, iterative weight updates, and perceptron behavior in a practical example.

Q & A

What is the main topic discussed in the video?

The video explains how to calculate and train a simple perceptron neural network with two input features and one binary output using supervised learning.

How many training data samples are used in the tutorial?

The tutorial uses 7 training data samples, each containing two features (X1 and X2) and one output label (Y).

What are the initial weights used in the perceptron model?

The initial weights are W1 = 0.3 and W2 = -0.4.

What learning rate is used during the training process?

The learning rate used in the tutorial is 0.04.

Which activation function is applied in the perceptron?

The tutorial uses a sigmoid-like threshold approach where outputs greater than 0.5 are classified as 1, and outputs less than or equal to 0.5 are classified as 0.

How is the weighted sum calculated in the perceptron?

The weighted sum is calculated by multiplying each input feature by its corresponding weight and then adding the results together.

How is the prediction value (Ypred) determined?

If the weighted sum is greater than 0.5, the prediction becomes 1; otherwise, it becomes 0.

How is the error calculated in the tutorial?

The error is calculated as the absolute difference between the actual output value (Y) and the predicted value (Ypred).

What formula is used to update the weights?

The new weight is calculated using the formula: W_new = W_old + (learning_rate × error × input_feature).

Why are the weights updated during training?

Weights are updated to reduce prediction errors and improve the perceptron’s classification accuracy.

What happens when there is no error in a prediction?

If the prediction error is zero, the weights are not updated because the model already predicts correctly.

What is meant by an iteration in the tutorial?

An iteration refers to one complete pass through all training data samples during the learning process.

How many iterations were needed before the model achieved zero error?

The model achieved zero error at around the 10th iteration.

What testing data examples are used after training?

The testing examples include input pairs such as (0.3, 0.7) and (0.7, 0.6).

What prediction result was obtained for the testing data?

The perceptron predicted the testing data output as class 1.

Why does the tutorial continue iterations even after reaching zero error?

The tutorial continues iterations to demonstrate that once the model reaches zero error, the weights stop changing because no further corrections are needed.

What tool does the instructor use to perform the calculations?

The instructor uses a spreadsheet application to calculate sums, predictions, errors, and weight updates.

Why are spreadsheet cell references locked during calculations?

Cell references are locked so formulas can be dragged and copied correctly across multiple rows without changing important parameter references.

What is the purpose of supervised learning in this tutorial?

Supervised learning is used so the perceptron can learn from labeled training data and improve its predictions over time.

What future topic does the instructor mention at the end of the video?

The instructor mentions experimenting with different parameters, such as learning rates and iteration counts, in future videos.