Understand in one article: the connections and differences between AI, machine learning and deep learning

In today’s wave of rapid technological changes, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are like bright stars, leading the new wave of information technology. tide. These three words frequently appear in various cutting-edge discussions and practical applications, but for many explorers who are new to this field, their specific meanings and their internal connections may still be shrouded in mystery.

Let’s take a look at this picture first.

It can be seen that there is a close correlation and progressive relationship between deep learning, machine learning and artificial intelligence. Deep learning is a specific field of machine learning, which is an important component of artificial intelligence. The connections and mutual promotion between these fields enable the continuous development and improvement of artificial intelligence technology.

What is artificial intelligence?

Artificial Intelligence (AI) is a broad concept whose main goal is to develop computing systems that can simulate, extend or even surpass human intelligence. It has specific applications in many fields, such as:

• Image Recognition (Image Recognition) is an important branch of AI, which is dedicated to studying how to enable computers to obtain data through visual sensors and perform operations based on these data. Analysis to identify objects, scenes, behaviors and other information in images, simulating the recognition and understanding process of visual signals by the human eye and brain.
• Natural Language Processing (NLP) is the ability of computers to understand and generate human natural language. It covers a variety of tasks such as text classification, semantic analysis, machine translation, etc., and strives to simulate human listening. Talk about intelligent behaviors in reading and writing.
• Computer Vision (CV) includes image recognition in a broader sense. It also involves image analysis, video analysis, three-dimensional reconstruction and other aspects. It aims to allow computers to extract information from two-dimensional or three-dimensional images. "Seeing" and understanding the world is a deep imitation of the human visual system.
• Knowledge Graph (KG) is a structured data model used to store and represent entities and their complex relationships with each other. It simulates the accumulation and development of human knowledge in the cognitive process. The ability to utilize knowledge and the process of reasoning and learning based on prior knowledge.

These high-end technologies are researched and applied around the core concept of “simulating human intelligence”. They focus on the development of different perception dimensions (such as vision, hearing, thinking logic, etc.), and jointly promote the continuous development and progress of artificial intelligence technology.

What is machine learning?

Machine Learning (ML) is a crucial branch in the field of artificial intelligence (AI). It uses various algorithms to enable computer systems to automatically learn rules and patterns from data to make predictions and decisions, thereby enhancing and expanding the capabilities of human intelligence.

For example, when training a cat recognition model, the machine learning process is as follows:

• Data preprocessing: First, preprocess a large number of collected cat and non-cat pictures. Processing, including scaling, grayscale, normalization and other operations, and converting the image into a feature vector representation. These features may come from manually designed feature extraction techniques, such as Haar-like features, local binary patterns (LBP) or Feature descriptors commonly used in other computer vision fields.
• Feature selection and dimensionality reduction: Select key features according to the characteristics of the problem, remove redundant and irrelevant information, and sometimes use PCA, LDA and other dimensionality reduction methods to further reduce feature dimensions and improve algorithm efficiency.
• Model training: Then use the preprocessed labeled data set to train the selected machine learning model, and optimize the model performance by adjusting the model parameters so that the model can distinguish given features. Pictures of cats and non-cats.
• Model evaluation and verification: After training is completed, the model is evaluated using an independent test set to ensure that the model has good generalization ability and can be accurately applied to new unseen samples.

The top 10 commonly used machine learning algorithms are: decision tree, random forest, logistic regression, SVM, naive Bayes, K nearest neighbor algorithm, K-means algorithm, Adaboost algorithm, neural network, Marr Koff et al.

What is deep learning?

Deep Learning (DL) is a special form of machine learning. It simulates the way the human brain processes information through a deep neural network structure, thereby automatically extracting complex feature representations from the data.

For example, when training a cat recognition model, the deep learning process is as follows:

(1) Data preprocessing and preparation:

• Collect a large number of A dataset containing cat and non-cat images, cleaned and annotated to ensure that each image has a corresponding label (such as "cat" or "non-cat").
• Image preprocessing: Adjust all images to a uniform size, perform normalization processing, data enhancement and other operations.

(2) Model design and construction:

• Choose a deep learning architecture. For image recognition tasks, Convolutional Neural Network (CNN) is usually used. CNN can effectively extract local features of images and abstract them through multi-layer structures.
• Build a model hierarchy, including convolutional layers (for feature extraction), pooling layers (to reduce the amount of calculation and prevent overfitting), fully connected layers (to integrate and classify features), and possible batch reduction Unified layer, activation function (such as ReLU, sigmoid, etc.).

(3) Initialization parameters and setting hyperparameters:

• To initialize the weights and biases of each layer in the model, random initialization or a specific initialization strategy can be used.
• Set hyperparameters such as learning rate, optimizer (such as SGD, Adam, etc.), batch size, training period (epoch), etc.

(4) Forward propagation:

• Input the preprocessed image into the model, and perform operations such as convolution, pooling, and linear transformation at each layer , and finally obtain the predicted probability distribution of the output layer, that is, the probability that the model determines that the input picture is a cat.

(5) Loss function and backpropagation:

• Use the cross-entropy loss function or other suitable loss function to measure the difference between the model prediction results and the real label difference.
• After calculating the loss, execute the back propagation algorithm to calculate the gradient of the loss with respect to the model parameters in order to update the parameters.

(6) Optimization and parameter update:

• Use gradient descent or other optimization algorithms to adjust model parameters based on gradient information, with the purpose of minimizing the loss function.
• During each training iteration, the model will continue to learn and adjust parameters, gradually improving its ability to recognize cat images.

(7) Verification and evaluation:

• Regularly evaluate the model performance on the verification set, monitor changes in accuracy, precision, recall and other indicators to This guides hyperparameter tuning and early stopping strategies during model training.

(8) Training completion and testing:

• When the model’s performance on the validation set becomes stable or reaches the preset stopping conditions, stop training.
• Finally, evaluate the generalization ability of the model on an independent test set to ensure that the model can effectively identify cats on new unseen samples.

The difference between deep learning and machine learning

The difference between deep learning and machine learning is:

1. Method of solving problems

Machine Learning algorithms usually rely on human-designed feature engineering, that is, key features are extracted in advance based on background knowledge of the problem, and then models are built based on these features and optimized solutions are performed.

Deep learning adopts an end-to-end learning method, automatically generating high-level abstract features through multi-layer nonlinear transformation, and these features are continuously optimized during the entire training process, without manual selection and construction of features. Closer to the cognitive processing method of the human brain.

For example, if you want to write a software to identify a car, if you use machine learning, you need to manually extract the characteristics of the car, such as size and shape; and if you use deep learning, Then the artificial intelligence neural network will extract these features on its own, but it requires a large number of pictures labeled as cars to learn.

2. Application Scenarios

The application of machine learning in fingerprint recognition, characteristic object detection and other fields has basically met the requirements of commercialization.

Deep learning is mainly used in text recognition, face technology, semantic analysis, intelligent monitoring and other fields. At present, it is also rapidly deploying in intelligent hardware, education, medical and other industries.

3. Required amount of data

Machine learning algorithms can also show good performance in small sample cases. For some simple tasks or problems where features are easy to extract, less data Satisfactory results can be achieved.

Deep learning usually requires a large amount of annotated data to train deep neural networks. Its advantage is that it can directly learn complex patterns and representations from the original data. Especially when the data size increases, the performance of the deep learning model improves. more significant.

4. Execution time

In the training phase, because the deep learning model has more layers and a large number of parameters, the training process is often time-consuming and requires the support of high-performance computing resources, such as GPU cluster.

In comparison, machine learning algorithms (especially those lightweight models) usually have smaller training time and computing resource requirements, and are more suitable for rapid iteration and experimental verification.

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