What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) refers to the implementation of tasks such as learning, reasoning, and perception, which can be performed by human intelligence, by computers. AI is broadly divided into rule-based AI, machine learning AI, and deep learning AI.
What is Deep Learning?
Deep Learning is a field of machine learning that mimics the neural networks of the human brain, allowing computers to learn on their own. Deep learning uses a multi-layer neural network structure to autonomously learn high-level abstract patterns from data.
| Traditional Programming | Machine Learning |
|---|---|
| Input Data | Input Data |
| Rules (Written by Humans) | Output (Results) |
| Output (Results) | Rules (Learned by Model) |
| In traditional programming, predetermined rules are used alongside input data to produce an output. The rules are explicitly programmed by humans, and the resulting output is based on this predefined logic. | |
| In machine learning, instead of humans writing the rules, the model infers the rules by analyzing input data and the corresponding output. It then uses these learned rules to predict new outputs from new input data. | |
Basic Concepts of Deep Learning
Artificial Neural Network (ANN)
- An artificial neural network is a mathematical model created by mimicking the neural cells of the human brain.
- It consists of input layers, hidden layers, and output layers.
- The nodes in each layer are connected through weights and activation functions.
Learning Methods
- Supervised Learning: The model learns by being provided with correct data (labels).
- Unsupervised Learning: The model learns to find patterns in data without labels.
- Reinforcement Learning: The model learns to find the optimal action through rewards.
Backpropagation Algorithm
- A method of updating weights by propagating errors from the output layer backward.
- Plays a crucial role in the learning of deep learning models.
Activation Function
- A function used to transform input signals into output signals at the nodes of a neural network.
- Includes sigmoid, ReLU (Rectified Linear Unit), tanh, etc.
Loss Function
- A function that represents the difference between the model’s output and the actual correct answer.
- The model adjusts the weights to minimize the loss function value during the learning process.
Deep learning involves a process of finding the point with the least loss through numerous iterations of learning and backpropagation.
Types of Deep Learning Models
Convolutional Neural Network (CNN)
- Suitable for image recognition and object detection.
- Extracts and classifies features of images using filters.
- Notable examples include AlexNet, VGGNet, GoogLeNet, ResNet, etc.
Recurrent Neural Network (RNN)
- Suitable for processing sequential data (text, voice, etc.).
- Remembers the state of hidden layers and uses the previous output as the next input.
- Variants include LSTM, GRU, etc.
Generative Adversarial Network (GAN)
- Consists of two neural networks, Generator and Discriminator, that learn through competition.
- Used for generating images, voice, text, etc.
Autoencoder
- An unsupervised learning model that compresses and restores input data.
- Used for dimensionality reduction, data compression, anomaly detection, etc.
Applications of Deep Learning
Computer Vision
- Used for image and video recognition, object detection, autonomous driving, etc.
- Primarily uses CNN models.
Natural Language Processing (NLP)
- Applied to machine translation, chatbots, sentiment analysis, etc.
- Uses models like RNN, Transformer, etc.
Speech Recognition
- Used to convert voice into text.
- Utilizes RNN, CNN, and other models.
Recommendation Systems
- Recommends products or content to users in online shopping malls, video streaming services, etc. based on user preferences.
Medical Field
- Applied to disease diagnosis, new drug development, medical image analysis, etc.
Deep learning continues to evolve and is being applied in various fields. It is expected that deep learning technology will become more sophisticated and significantly impact our lives in the future.
In the next post, we will explore how to code and create a Major League Baseball (MLB) simulation using Python.