Neural networks and deep learning are the cornerstone of modern AI technology and are used in many fields. This presentation will explain how these two technologies differ and how they are related.
What is deep learning?
Basic Concepts of Deep Learning
Deep learning, a type of machine learning, is a technology that automatically extracts features from complex data and learns by building a multi-layered structure of neural networks that mimic the neural circuits of the human brain. While conventional machine learning requires humans to design features, deep learning enables more advanced learning by automatically extracting features from data.
How Deep Learning Works
Deep learning works by automatically extracting features from complex data and learning by building a multi-layered neural network that mimics the neural circuits of the human brain. A neural network consists of an input layer, a hidden layer, and an output layer, with each layer consisting of multiple nodes. The nodes are connected by parameters called weights and biases, and by adjusting these parameters, the transformation from input data to output data is learned. In deep learning, this neural network can be multi-layered to extract more complex features.
Deep Learning Application Examples
Deep learning is used in a variety of fields, including image recognition, natural language processing, speech recognition, and machine translation. For example, in image recognition, deep learning has made it possible to accurately classify images by capturing minute features that cannot be distinguished by the human eye. In natural language processing, deep learning has made it possible to understand the meaning of sentences and generate natural, human-like sentences. In speech recognition, deep learning has made it possible to improve the accuracy of speech recognition even in noisy environments.
Basic Information on Neural Networks
Neural Network Components
A neural network is a model that mimics the neural circuits of the human brain, with a network structure consisting of multiple nodes (neurons) connected to each other. The main components are as follows
- input layer: This layer receives data from the outside. For image data, pixel values are input, and for text data, word vectors are input.
- hidden layer: A layer between the input and output layers that is responsible for extracting features from the data. There are often multiple hidden layers, and more complex features can be extracted by layering.
- output layer: This is the final layer of the network and outputs the training results. It outputs the probability of each class for classification problems and predictions for regression problems.
- node:Also called a neuron, it is the basic unit of a network. A node receives an input signal and produces an output signal by means of an activation function.
- weight: This parameter represents the strength of the connection between each node. The value of the weight changes the strength of signal transmission between nodes.
- bias: A parameter that affects the activation of a node. Bias is responsible for adjusting the node's output value.
Types of Neural Networks
There are many different types of neural networks, each with different characteristics and uses. Typical types of neural networks are listed below.
- Multilayer Perceptron (MLP): One of the most basic neural networks, it consists of an input layer, a hidden layer, and an output layer.
- Convolutional Neural Networks (CNN): A neural network specialized for image recognition, which extracts image features through a process called convolutional computation.
- Recurrent Neural Network (RNN): Neural network suitable for processing time-series data and can store and process past information.
- Recurrent Neural Network (RNN): Neural network suitable for processing time-series data and can store and process past information.
- Long Short Term Memory (LSTM):A type of RNN that can learn long-term dependencies.
- Generative Adversary Network (GAN):This technique generates more realistic data by pitting two neural networks against each other.
Practicality of Neural Networks
Neural networks have achieved practical results in a variety of fields.
- image recognitionFace Recognition, Object Detection, Medical Imaging, etc.
- natural language processingMachine translation, sentence summarization, sentiment analysis, etc.
- voice recognitionVoice assistants, voice search, voice input, etc.
- machine translation: Automatic translation services, language learning apps, etc.
- Game AIGame character behavior control, game balance adjustment, etc.
- financingMedical: Disease diagnosis, drug development, medical image analysis, etc.
Difference between Deep Learning and Neural Networks
Deep Learning and Neural Networks
Deep learning is a type of machine learning that uses neural networks. In other words, deep learning is a technology developed based on neural networks, and neural networks are the foundation of deep learning.
What are the main differences?
The main difference between deep learning and neural networks is the number of layers in a neural network. Neural networks typically have only one or two hidden layers, whereas deep learning can have dozens or even hundreds of hidden layers. The more layers, the more complex features can be extracted and the more advanced tasks can be performed.
| feature | neural network | deep learning |
| number of hidden layers | 1-2 layers | Dozens to hundreds of layers |
| special extraction | Requires human design | self-extracting (computer file) |
| amount of data | Can be studied in small quantities | Large amount of data required |
| Study Time | short | long (time) |
| scope (of a document) | Relatively simple problem | Complex Issues |
Which should be used?
Which one should be used depends on the complexity of the problem being handled and the amount of data.
- Simple problem: In some cases, a neural network is sufficient.
- Complex Issues: If more advanced feature extraction is required, deep learning is the way to go.
- Low data volume: Neural networks are easier to learn.
- high data volumeDeep learning gives better results.
The Role of Deep Learning in Machine Learning
Relationship between machine learning and deep learning
Deep learning is a type of machine learning, a technology that has greatly advanced the field of machine learning. Machine learning is a technique that allows computers to learn from data and perform tasks such as prediction and classification. Deep learning is a method of machine learning that uses neural networks, which mimic the workings of the human brain to achieve more advanced learning.
Why Deep Learning is Needed
Deep learning is needed because it can solve complex problems that cannot be solved by traditional machine learning. Conventional machine learning requires humans to design features, but deep learning enables more advanced learning by automatically extracting features from data. For example, in image recognition, deep learning makes it possible to accurately classify images by capturing minute features that cannot be distinguished by the human eye. This is something that could not be achieved with conventional machine learning.
Real-world applications
Deep learning is deeply embedded in our daily lives and is bringing revolutionary change.
Deep Learning in Image Recognition
Deep learning has achieved great success in the field of image recognition. For example, in face recognition, deep learning has made it possible to accurately capture human facial features and identify individuals. In object detection, deep learning has made it possible to accurately detect objects in images. Furthermore, self-driving cars can recognize pedestrians and signs on the road in real time to ensure safe driving. These technologies are being used in a variety of fields, including security systems, medical imaging, and automated driving.
Use in natural language processing
Deep learning has also made great strides in the field of natural language processing. For example, in machine translation, deep learning has made it possible to achieve more natural translations. In business, systems that automatically extract and summarize important information from large volumes of documents have dramatically improved operational efficiency. Furthermore, in customer service, AI chatbots respond to customer inquiries 24 hours a day, enabling natural conversations that resemble those of human operators.
Evolution of Speech Recognition Technology
Deep learning has also made great strides in the field of speech recognition. For example, deep learning has made it possible for voice assistants to accurately recognize and respond to human speech. In voice search, deep learning has made it possible to search by voice. These technologies are used in various fields such as voice assistants, voice search, and voice input.
Pattern Analysis in the Financial Sector
In the financial sector, systems that analyze complex patterns in market data and support investment decisions have been put to practical use. Fraudulent transaction detection systems detect unusual transaction patterns in real time, contributing to the prevention of financial crimes.
Innovations in the Medical Field
Its use in the medical field has attracted particular attention: AI-based diagnostic support systems analyze vast amounts of medical data and help doctors improve the accuracy of their diagnoses. In the development of new drugs, promising candidate substances are predicted based on analysis of molecular structures, shortening development time and reducing costs. Deep learning also plays an important role in realizing personalized medicine that takes into account the genetic information and lifestyle habits of individual patients.
In this way, the use of deep learning is spreading to all aspects of our lives and contributing greatly to solving social issues. And as the technology continues to evolve, new ways of utilizing it are being created every day.
Future Prospects for Deep Learning
The future of deep learning is full of innovative possibilities far beyond our wildest imaginations, and beyond 2024, this technology is predicted to dramatically transform every aspect of society.
Technological evolution and future possibilities
Deep learning is breaking through the limitations of traditional machine learning to make more advanced and complex tasks feasible. Of particular note is its integration with quantum computing. Advances in quantum algorithms and hardware are enabling quantum-enhanced deep learning models with unprecedented capabilities.
In the medical field, AI-based diagnostic support systems are realizing personalized medicine that takes into account the genetic information and lifestyle habits of individual patients, dramatically improving the accuracy of treatment. In the development of new drugs, promising candidate substances are predicted based on analysis of molecular structures, dramatically shortening development time and reducing costs.
In automated driving technology, it has been suggested that advances in deep learning could reduce traffic accidents by up to 901 TP3T. Furthermore, the widespread use of self-driving cars is expected to reduce carbon emissions by as much as 601 TP3T.
Industry Impact and Innovation
Deep learning is expected to have a significant impact on a variety of industries. For example, in the manufacturing industry, deep learning is expected to improve quality control and production efficiency. In the financial industry, deep learning is expected to be used for investment forecasting and fraud detection. In the medical industry, deep learning is expected to be used for disease diagnosis and drug development.
Deep Learning Challenges and Solutions
While modern deep learning is an innovative technology, it faces several important challenges. Researchers and engineers are proposing and implementing various solutions to these challenges.
Dealing with Model Complexity
Deep learning models suffer from the problem of overlearning (overfitting). This is a phenomenon in which the model "memorizes" the training data, resulting in poor generalization performance to new data. To address this issue, techniques such as dropout and L1/L2 regularization can be introduced to improve the generalization performance of the model.
Computational resources and training time
Training deep learning models requires a huge amount of computational resources and time. Especially for small and medium-sized enterprises, procurement of expensive hardware and lengthy learning processes are a major burden. To address this problem, it is effective to utilize cloud services, deploy distributed learning systems, and employ optimization techniques such as model quantization and branch pruning.
Data Collection and Quality Control
Collecting high-quality training data remains a major challenge. In the case of image recognition, hundreds of millions of images may be required.
For this issue:
- Utilization of data augmentation technology
- Introduction to transfer learning
- Adoption of semi-supervised learning
- Use of Crowdsourcing
Solutions such as the following have been proposed
black-box problem
The opaqueness of the decision-making process in deep learning models is a major challenge, especially in critical decision-making situations.
For this issue:
- Research and development of Explainable AI (XAI)
- Use of visualization tools
- Introduce methods to improve model interpretability
and other initiatives are underway.
catastrophic oblivion
Catastrophic forgetting, in which past learning is lost when new learning begins, is another important issue.
For this issue:
- Introduction of review mechanism
- Conducting a simulated rehearsal
- EWC (Elastic Weight Consolidation)
and other measures have been effective.
These challenges are gradually being resolved as technology advances, but it is believed that a complete solution will still take time. When companies implement deep learning, it is important to fully understand these challenges and take appropriate measures.
Summary: Explains the difference between deep learning and neural networks
Deep learning is a type of machine learning that uses neural networks to mimic the workings of the human brain. Deep learning is used in various fields such as image recognition, natural language processing, and speech recognition, and is expected to continue to evolve and create new possibilities in various fields.
However, deep learning also presents challenges in terms of data volume, training time, and explainability. By solving these challenges, deep learning will become a more practical technology and has the potential to significantly change our lives.
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