Kalau bukan Neural Network? | Pengenalan Traditional Machine Learning

Summary

This video explores the two main methods in machine learning: deep learning and traditional machine learning. Deep learning, inspired by human neural networks, automatically learns from data and is highly accurate but requires powerful hardware and time. In contrast, traditional machine learning relies on the programmer to analyze data and select appropriate models. It is faster and more suitable for limited resources or when interpretability is key. Despite deep learning's popularity, traditional machine learning remains relevant for simpler tasks and smaller datasets. The video encourages viewers to subscribe for more insights.

Takeaways

• 😀 Artificial Neural Networks (ANN) or Deep Learning are inspired by human neural networks.
• 😀 Traditional Machine Learning methods do not rely on deep learning as their core algorithm.
• 😀 Both Deep Learning and Traditional Machine Learning are mathematical models, but with different approaches.
• 😀 Deep Learning learns automatically from data using backpropagation, while Traditional Machine Learning requires manual analysis of data characteristics.
• 😀 In Traditional Machine Learning, we select the mathematical model that best fits the data, based on manual analysis.
• 😀 A simple example: predicting someone's weight based on height using a linear relationship between the two variables.
• 😀 AI in Traditional Machine Learning finds the best-fitting line for the data, making predictions based on it.
• 😀 Traditional Machine Learning can also be used for classification tasks, such as separating two different classes with a line.
• 😀 Deep Learning is popular for its accuracy, but Traditional Machine Learning is still relevant and useful today, especially when deep learning is hard to implement.
• 😀 Traditional Machine Learning is faster and requires less computational power compared to Deep Learning, making it more accessible in certain situations.

Q & A

What is traditional machine learning?

Traditional machine learning refers to machine learning techniques that do not rely on deep learning as their core method. It involves manually analyzing the data and selecting the appropriate mathematical models for processing it.

How does traditional machine learning differ from deep learning?

Traditional machine learning requires human input to analyze data characteristics and choose models, while deep learning automatically learns from data using backpropagation without human intervention.

What is an example of traditional machine learning?

An example of traditional machine learning is predicting a person's weight based on their height. The relationship can be represented by a straight line, and the model would predict the weight based on a given height.

What type of learning is used in the weight and height prediction example?

The example of predicting weight from height is an instance of supervised learning, where the input data (height) is paired with the correct output (weight).

Can traditional machine learning be used for classification tasks?

Yes, traditional machine learning can be used for classification tasks, such as separating data into two distinct classes by finding the best fitting line that divides the data.

Why might traditional machine learning still be preferred over deep learning?

Traditional machine learning can be preferred when deep learning is difficult to apply, such as when there are limitations in hardware or when the required amount of data is not available.

How long does deep learning training typically take compared to traditional machine learning?

Deep learning training can take hours, days, or even weeks, depending on hardware, whereas traditional machine learning typically requires only seconds to hours for training.

What are the challenges that make deep learning less feasible for some users?

Deep learning can be challenging due to high computational requirements and the need for extensive hardware. It also may require a large amount of data and longer training times.

What role does interpretability play in choosing between traditional machine learning and deep learning?

Interpretability is important when the user needs to understand how the model makes predictions. Traditional machine learning models tend to be more interpretable, making them a better choice in some situations.

What should viewers do if they want to learn more about traditional machine learning?

Viewers are encouraged to subscribe to the channel for future updates and detailed explanations on traditional machine learning and other related topics.