In recent years, the world of artificial intelligence has undergone a major revolution known as deep learning. However, before deep learning reached its current peak, there was a fundamental technology that became its forerunner: neural networks. Want to learn more about neural networks? Stay tuned!
Hello, dear friends, how are you now? I hope you're all healthy and happy! Welcome back, Angel, to discuss the latest topics related to machine learning and its friends. Hey, who knows the regional language used in that paragraph? The clue is a city famous for its signature dish, rendang!
Hmm, before we get into anything new, I'm curious: what are you currently interested in learning? If you're exploring machine learning or deep learning, I think this article is perfect for you! So, stay tuned until the end and don't go anywhere! Who knows, what we'll discuss this time will answer the question you've been searching for!
Before we go any further, have you read our blog about deep learning? There, I thoroughly discussed the definition, concepts, architecture, and various examples of its use.
In that blog, we also frequently encountered the term "neural network," but unfortunately, we didn't discuss it in depth. So, if you haven't read it, don't forget to read it to understand our purpose in discussing neural networks here!
What is Deep Learning?
The term "Deep Learning" used by the Minister of Elementary and Secondary Education is not the same as the term "Deep Learning" commonly used in the field of Artificial Intelligence (AI). In the educational context, Deep Learning is a learning approach that emphasizes in-depth conceptual understanding and mastery of competencies within a narrower scope of material.
In Deep Learning, students are encouraged to actively engage in the learning process and delve into the topic being studied, allowing them to explore more deeply and enjoy the beauty of the topic's panorama.
The Deep Learning approach contrasts with the Surface Learning approach, which attempts to cover a large amount of material at the expense of understanding and improving students' competencies. Students are ultimately forced to memorize a large amount of material without being able to understand, own, or enjoy the learning process.
What are the key features of deep learning?
According to the Minister of Elementary and Secondary Education, Abdul Mu'ti, the Deep Learning approach can be achieved through three main elements: Meaningful Learning, Mindful Learning, and Joyful Learning.
Through the Meaningful Learning process, students can gain meaning in what they are learning. Furthermore, through Mindful Learning, students can become active agents who consciously intend to develop their understanding and competencies. The Joyful Learning process motivates students to engage in the learning process.
Let's discuss these three elements in more depth!
1. Meaningful Learning
The Meaningful Learning theory, proposed by David Ausubel, explains a learning process in which teachers help students connect new concepts to concepts they already understand. This Meaningful Learning process aims to make learning more meaningful for students.
For example, to introduce adding fractions, we can start by adding more concrete objects.
1 chicken + 2 chickens = 3 chickens1 ball + 2 balls = 3 balls1 fifth + 2 fifths = 3 fifths → ⅕ + ⅖ = ⅗
Or
1 chicken + 2 ducks = 1 bird + 2 birds = 3 birds1 dozen + 2 codes = 12 pieces + 40 pieces = 52 pieces1 half + 2 thirds = 3 sixths + 4 sixths = 7 sixths
2. Mindful Learning
Mindful Learning is often known as metacognition in educational theory. In Mindful Learning, students are invited to always be aware of the learning process they are undergoing. This awareness consists of several aspects:
- Awareness of what they already understand or have mastered.
- Awareness of what they have not yet understood or mastered.
- Awareness of the importance of understanding or mastering the competencies of what they are currently learning.
- Awareness of the flow of the learning process they are currently undergoing to achieve the understanding or competency they desire.
- Awareness of progress in understanding or competency after reflecting on the learning process they have gone through.
- Awareness of things that can still be explored further in the next learning process.
Thus, students are guided to become active agents responsible for their own learning process. Unlike adults, this awareness does not arise automatically in children, so teachers must continuously foster this awareness from the beginning to the end of the learning process.
For example, teachers can encourage students to always draw their own conclusions from their learning at the end of a teaching session and reflect on the development of their understanding or competency. Through this reflection process, students can understand their individual strengths and weaknesses and have clearer goals for future learning.
3. Joyful Learning
Joyful Learning emphasizes the importance of creating a positive learning environment so that students can enjoy every part of the learning process.
For example, a learning approach through games or interactive activities can make students more enthusiastic about learning.
What is neural network in simple words?
First, let's take a look back! You still remember this image, right?
From the image above, we can see that deep learning is a subset of neural networks, which serve as the foundation for deep learning. Neural networks consist of interconnected neurons in multiple layers, while deep learning involves the use of deeper and more complex neural networks to solve more complex problems.
A neural network, or artificial neural network, is a mathematical model inspired by the workings of the human brain. Neural networks are designed to mimic the way the human brain processes information, using simple units called neurons.
A neural network, also known as an artificial neural network, is a computing system inspired by the way the human brain works. They consist of layers of interconnected neurons and aim to intelligently process data. But wait, how do neural networks work?
Imagine your brain trying to recognize a friend's face in a crowd. Your brain doesn't work linearly; it processes various information such as facial shape, hair color, and even expressions to identify that person. Neural networks operate on a similar principle, only performed by machines.
Neural network methods are highly effective in a variety of applications, from speech recognition in smartphones, spam filters in emails, to movie recommendations on streaming platforms. That's why it's crucial to understand what neural networks are and how they impact everyday life.
Neurons are the basic units in artificial neural networks, responsible for processing information. Neurons work by receiving signals or data, performing calculations, and producing output that is used to make decisions or predictions.
These networks consist of multiple layers of interconnected neurons, and information flows through these layers to complete tasks, such as recognizing patterns, classifying data, or making predictions based on the given data.
Neural networks consist of three main types of layers: the input layer, the hidden layer, and the output layer. The input layer receives initial data, the hidden layer processes the data through various neurons, and the output layer produces the final result based on the processing performed by the previous layers. Each neuron in a neural network calculates a value based on assigned weights and then forwards the result to other neurons in the network.
Biological Neurons vs. Artificial Neurons
Biological neurons are nerve cells in our bodies that function to communicate and process information. These neurons have important parts, such as dendrites, which receive signals from other neurons; a nucleus or soma, which collects signals; and a nucleus or soma, which collects signals. axons, which transmit signals to other neurons or muscles; and axon terminals, which propagate the signals. Their primary function is to enable us to think, move, and feel.
On the other hand, artificial neurons are elements in computer models inspired by the workings of biological neurons. Artificial neurons receive weighted input data, plus a bias value. This processed data is then processed through an activation function to produce an output. Artificial neurons are used in machine learning and deep learning technologies for tasks such as recognizing images, classifying text, or making predictions.
Wow! It's fascinating to realize how similar the structures of biological and artificial neurons are. This connection shows how nature teaches us valuable lessons, and how humankind's extraordinary creations can replicate these miracles in modern technology. Pretty cool, isn't it?
Simple Neural Network: Single Perceptron
The simplest or most basic neural network is a single perceptron unit, known as a single perceptron. Despite its simplicity, the perceptron plays a crucial role in understanding how neural networks work.
The perceptron is the fundamental building block of neural networks. Frank Rosenblatt of the Cornell Aeronautical Library was the scientist who first invented the perceptron in 1957. The perceptron was inspired by neurons in the human brain's neural network. In artificial neural networks, perceptrons and neurons refer to the same thing.
Each perceptron consists of several components, each with its own function, as follows:
a. Inputs
The perceptron receives one or more input values. Each input value represents the information the perceptron wants to process.
b. Weight (w)
Each input is multiplied by its corresponding weight. This weight is a number that indicates how much influence each input has on the final result. For example, if the input is data from a particular feature in a dataset, the weight determines how important that feature is in the calculation process.
c. Biased
Bias is an additional value added to the calculated input result that has been multiplied by the weight. Bias helps the perceptron adjust the final result and makes the model more flexible. Bias acts as a regularizer so the model can make better predictions.
d. Net Input or Linear Function (z)
The result of multiplying each input by its weight, then summing all the results and adding the bias, is called the net input.
e. Activation Function (f)
After obtaining the net input 𝑧, this value is processed through an activation function. An activation function is a formula that determines how the net input is transformed into the perceptron output. This function adds non-linearity to the model and helps the neural network process more complex patterns. Commonly used activation functions are the step function for binary classification or the sigmoid function and ReLU (Rectified Linear Unit) for other applications.
f. Output (y)
The output 𝑦 is the final result of the perceptron after going through the activation function. This output shows the result of the calculation and prediction process performed by the perceptron. For example, in a classification task, this output could be a label indicating the data category.
How does the Perceptron work?
First, the perceptron receives input in the form of numbers from the data it wants to process. Each number has a weight indicating its importance to the final decision. The weights are the components that will be studied during perceptron training to determine the contribution of each input.
Next, the perceptron sums the inputs multiplied by their respective weights. This process also involves adding a bias value, which serves as an additional number to adjust the final result. The bias helps shift the activation function curve, making it more flexible and reducing errors. The result of this summation is often referred to as the weighted sum.
Next, the perceptron applies an activation function to the weighted sum. This activation function converts the sum into a more useful final value, such as a number in the range (0, 1) or (-1, 1). This function allows the perceptron to adapt and handle non-linear data patterns.
Finally, the resulting output is the final result of the perceptron's calculations, a numeric value representing a decision or prediction based on the given input.
What Are Neural Network Methods?
When we refer to the term "neural," it comes from the word "neuron," which is the basic unit of the brain and nervous system. In the context of neural networks, the word "neural" refers to an artificial neural network that mimics the way neurons in our brains work.
Neurons in the human brain communicate with each other through electrical and chemical signals. In a neural network, artificial neurons communicate by sending signals in the form of numbers to each other. This process allows the machine to "learn" from the data it receives.
There are various neural network methods frequently used in AI and machine learning development. Some of the most popular include:
- Perceptron: This is the simplest type of neural network. It consists of only one layer of neurons and is used for simple tasks.
- Multilayer Perceptron (MLP): This is a more complex version of the perceptron with multiple layers. MLPs are used for more complex tasks, such as pattern recognition and classification.
- Convolutional Neural Network (CNN): CNNs are very popular in image and video processing. They use convolutional layers that can detect features such as edges, textures, and objects in images.
- Recurrent Neural Network (RNN): RNNs are used for sequential data, such as text and sound. They have the ability to "remember" previous information, making them ideal for tasks like natural language processing.
Each method has its own advantages and disadvantages, depending on the type of data being processed and the ultimate goal.
Neural Networks, Artificial Neural Networks, and Deep Learning
At the beginning of this discussion, we mentioned that neural networks (NNs) are the precursors to artificial neural networks (ANNs) and deep learning. However, to understand them more deeply, it's important to understand the differences between the three.
1. Neural Network
A neural network is a general term referring to models inspired by the way the human brain processes information. These neural networks can be of various types and complexities, ranging from very simple to complex.
The basic concept of a neural network involves interconnected processing units, similar to the neurons in the human brain. A single perceptron is the simplest example of a neural network. It is a model with a single layer of neurons that performs a simple classification task.
2. Artificial Neural Network
An artificial neural network (ANN) is a specific implementation of the neural network concept in the form used in computing and artificial intelligence. An ANN consists of basic units called artificial neurons, organized into layers. Each neuron receives input, processes it, and produces output. ANNs are used for various applications, such as pattern recognition, classification, and regression.
Key Characteristics
- Input Layer: Receives initial data.
- Hidden Layer: Processes data through neurons.
- Output Layer: Produces the final result of the processing.
Typically, ANNs consist of only 1 to 3 hidden layers, and this is often sufficient to solve various data processing and classification tasks.
3. Deep Learning
Deep learning is a subcategory of machine learning that uses very deep ANN structures, known as deep neural networks. Deep learning involves neural networks with many hidden layers, which allow the model to learn and recognize very complex and abstract patterns from data.
Key Characteristics
- Multiple Layers: Deep learning uses networks with many hidden layers, often more than 10 layers, which form a deep neural network.
- Feature Extraction Capability: With many layers, deep learning models can extract more complex and abstract features from data.
- Applications: Used in advanced applications, such as facial recognition, automatic language translation, and object detection in images.
What are the applications of neural network?
Neural networks, the foundation of deep learning, have a wide range of highly useful applications in various technological fields. Here are some examples of their main applications.
- Image Recognition: Neural networks are used to identify and classify objects in images. For example, a facial recognition system on a smartphone or an app that identifies a flower from a photo.
- Text Classification: This technology is capable of classifying text into specific categories. Examples include sentiment analysis in product reviews or spam email filtering.
- Prediction: Neural networks can predict future trends or values based on existing data. This is often used in stock market analysis, weather forecasting, and product demand forecasting.
- Natural Language Processing (NLP): This includes automatic language translation, chatbots that can communicate with users, and content recommendation systems.
- Anomaly Detection: In cybersecurity, neural networks can detect suspicious or anomalous behavior that may indicate an attack or data breach.
The Deep Learning Revolution and the Future of Neural Networks
With the development of deep learning, neural networks have undergone a major revolution. Now, we can train much deeper and more complex models, capable of handling tasks previously considered impossible for machines.
However, with all their advantages, neural networks also pose ethical and technical challenges. The use of artificial neural networks in critical decision-making, such as medical diagnosis or credit assessment, requires greater transparency and reliability.
Going forward, machine learning neural networks will continue to evolve and become more integrated into our lives. By understanding what neural networks are and how they work, we can be better prepared for the future of increasingly sophisticated technology.
Conclusion
Neural network methods are techniques used in machine learning to model the relationship between inputs and outputs based on data. This term is often associated with deep learning, a branch of machine learning that uses multi-layered neural networks to handle complex tasks.
A common example of neural networks is in image and speech recognition. For example, when you use an image search feature on the internet or a voice assistant like Siri or Google Assistant, a neural network is working hard behind the scenes to process and understand the input you provide.
Within a neural network, various algorithms aid the machine learning process. These neural network algorithms enable machines to "learn" from existing data, improve themselves, and provide more accurate results over time.
We've now explored the key topics surrounding neural networks, ANNs, and deep learning. With this understanding, you should have a clearer understanding of the differences between the three and how they play a role in the world of artificial intelligence.
Don't forget to always remember the concepts of machine learning, both supervised and unsupervised, as both are essential foundations for building effective models. The good news is, you can find in-depth material on deep learning implementation in the Learn Machine Learning Development class! This class is designed to provide a comprehensive and hands-on understanding that will help you master these concepts...
