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The relationship between artificial intelligence, machine learning and deep learning

Popularity：50 ℃/2025-01-15 16:00:50

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are three important fields in the development of modern science and technology. They have not only attracted widespread attention in academia, but have also been widely used in many industries. Although there is considerable overlap between them, their respective definitions and application scenarios are different. This article will provide an in-depth explanation of the relationship between the three, the main classifications, the role of deep learning, the workflow of machine learning, and their applications in the real world.

The relationship between artificial intelligence, machine learning and deep learning

The relationship between artificial intelligence, machine learning, and deep learning can be visualized by the following tree diagram:

Artificial Intelligence (AI)
                |
       --------------------------
       | |
   Machine Learning (ML) Non-Machine Learning Methods
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   --------------------------------------------------  ---
   | | |
 Supervised learning (Supervised) Unsupervised learning (Unsupervised) ……
   |
   --------------------------------------------------  ----------
   | | |
 Deep Learning Reinforcement Learning ……

explain:

  • Artificial Intelligence (AI)It is a general concept that refers to the ability of all machines to simulate human intelligence, including perception, reasoning, learning and decision-making. That is, everything that is done manually by machines is artificial intelligence. The goal of AI is to enable machines to handle and solve complex tasks.

  • Machine Learning (ML)It is a subset of artificial intelligence that refers to allowing machines to "learn" and improve automatically through data and experience without the need for explicit programming. That is to allow machines to learn rules based on existing data like humans. The goal of machine learning is to find rules or patterns in data through algorithms.

  • Deep Learning (DL)It is a branch of machine learning that uses neural networks, especially multi-layer neural networks, to process and analyze data. By simulating the structure and function of the human brain, deep learning can handle more complex tasks, especially in the context of big data and high computing power, showing great potential.

machine learning

The core idea of ​​machine learning is to train a model through data so that the model can make predictions or classifications. According to the annotation information in the learning process, machine learning can be divided into the following types:

Supervised Learning

Supervised learning is the most common machine learning method, and its basic feature is the use of labeled training data sets. The model learns the mapping relationship between input and output through these labeled data, so that it can predict new data. Common supervised learning tasks include classification and regression.

  • Classification problem: For example, given a picture, the model needs to determine whether it belongs to a cat or a dog.
  • regression problem: For example, predict the price of a house, given a series of characteristics (such as area, location, etc.).

Common algorithms: Linear regression, decision tree, support vector machine (SVM), K-nearest neighbor (K-NN), etc.

Unsupervised Learning

The difference between unsupervised learning and supervised learning is that it does not have labeled training data, and the model can only find the inherent structure or pattern from the input data. Common unsupervised learning tasks include clustering and dimensionality reduction.

  • clustering problem: For example, customers are divided into different groups based on their purchasing behavior.
  • Dimensionality reduction problem: For example, find the most important features in large-scale data sets and reduce the complexity of the data.

Common algorithms: K-means clustering, principal component analysis (PCA), autoencoders (Autoencoders), etc.

Reinforcement Learning

Reinforcement learning is a learning method that allows an agent to learn optimal strategies through interaction with the environment. The agent gradually optimizes its behavioral strategy by obtaining rewards or punishments through continuous trial and error. This learning method is mainly used in fields that require decision-making and strategy optimization.

Common applications: Game AI (such as AlphaGo), autonomous driving, robot control, etc.

The role of deep learning in machine learning

Deep learning (DL) is a subfield of machine learning that simulates the structure of the human brain to handle complex tasks by building multi-layered neural networks. Deep learning can process large amounts of unstructured data (such as images, audio, and text) and exhibit superior performance in complex pattern recognition tasks.

Advantages of deep learning：

  • Automatic feature extraction: Traditional machine learning models often require manual selection and extraction of features, while deep learning models can automatically extract features from raw data, which is especially important for complex data (such as images and speech).
  • Adapt to big data: Deep learning can train more accurate models under massive amounts of data, especially when the amount of data is huge, it performs better.
  • Dealing with non-linear problems: Deep neural networks can handle complex nonlinear problems through multiple levels of nonlinear transformation.

Common applications：

  • image recognition: Convolutional neural network (CNN) performs well in tasks such as image classification and target detection.
  • natural language processing: Recurrent neural network (RNN) and its variants (such as LSTM, GRU) have made significant progress in text generation, machine translation, etc.
  • Speech recognition and generation: For example, deep neural network (DNN) and self-attention mechanism (Transformer) are widely used in speech-to-text conversion, speech synthesis and other fields.

Machine Learning Jobs with Python

A typical process for doing machine learning work is as follows:

  1. problem definition: Clarify the goal of the task, such as classification, regression, etc.
  2. Data collection and preprocessing: Collect relevant data and perform data cleaning and preprocessing, including denoising, filling in missing values, standardization, feature extraction, etc.
  3. Choose models and algorithms: Choose an appropriate machine learning algorithm such as supervised learning, unsupervised learning, or deep learning based on the nature of the problem.
  4. Training model: Input training data into the selected model and adjust the model parameters through an optimization algorithm (such as gradient descent).
  5. Evaluate and tune: Use the validation set to evaluate model performance and adjust model hyperparameters based on the results.
  6. Deploy and apply: Deploy the trained model to the production environment for real-time prediction.

Why has Python become the most popular machine learning language?

Python has become the language of choice in the field of machine learning, mainly because of its following advantages:

  • Easy to learn and use: Python's syntax is concise and intuitive, and the learning curve is gentle, making it very suitable for beginners.
  • Powerful scientific computing library: Such as NumPy, Pandas, Matplotlib, etc., making data processing, analysis and visualization simple.
  • Rich machine learning library: Such as Scikit-learn, TensorFlow, PyTorch, etc., provide efficient algorithm implementation and tools, lowering the threshold for machine learning development.

Commonly used machine learning frameworks:

  • Scikit-learn: Suitable for traditional machine learning algorithms, simple and easy to use.
  • TensorFlow: A deep learning framework developed by Google and widely used in production environments.
  • PyTorch: A deep learning framework developed by Facebook, which is highly flexible and suitable for research and experiments.
  • Keras: An advanced neural network API based on TensorFlow that simplifies model building.

Artificial intelligence closest to us

Artificial intelligence has penetrated deeply into our daily lives. Here are some of the closest applications to us, which involve multiple machine learning and deep learning technologies mentioned above:

ChatGPT (Natural Language Processing)

ChatGPT belongs todeep learningfield, especially natural language processing (NLP) technology. It is trained through a large amount of text data and generates text based on the Transformer architecture. The applications of ChatGPT include automatic customer service, voice assistant, text generation, etc.

Image generation (such as DALL·E)

Image generation belongs todeep learningGenerative models in , especially techniques such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE). DALL·E realizes automated creative design and art generation by converting natural language descriptions into images.

Chess AI (such as AlphaGo)

AlphaGo isdeep learningandreinforcement learningcombination. It learns game strategies through deep neural networks and continuously optimizes strategies through reinforcement learning. It defeated the world's top Go players and demonstrated the power of AI in complex decision-making problems.

Face recognition (computer vision)

Facial recognition technology is mainly based ondeep learningConvolutional Neural Network (CNN) in . By learning a large number of face images, it can accurately recognize faces and perform identity verification. Application scenarios include mobile phone unlocking, security monitoring, financial payment, etc.

Content recommendation (such as Netflix recommendation system)

Content recommendation systems are widely used inmachine learningSupervised learning and deep learning in . It analyzes user behavior data to predict user preferences and recommend corresponding content. Algorithms typically include collaborative filtering, matrix factorization, and deep learning-based recommendation systems.

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