How to understand the difference between machine learning and deep learning?

Machine Learning and Deep Learning are both core technologies in the field of artificial intelligence (AI), but they have some key differences in concepts, methods, and applications.

difference in definition

• Machine learning (ML) is a subfield of artificial intelligence that studies the ability to automatically improve computer algorithms through data and experience. It relies on learning regularities or patterns in data through models to make predictions, classifications, or decisions.

• Deep learning (DL) is a subset of machine learning that focuses on using multi-layer (deep) neural networks to simulate the learning process of the human brain. Deep learning algorithms can automatically extract features and learn complex patterns from large amounts of data.

data dependency

• Machine learning : Traditional machine learning algorithms (such as linear regression, decision trees, support vector machines, etc.) usually require manual feature extraction. You need to understand the characteristics of the problem in advance and design the characteristics through expert knowledge or experience.

• Deep learning : Deep learning can automatically learn high-level features on very large data sets without manually designing features. It can learn appropriate representations directly from raw data such as images, audio, or text.

Model structure

• Machine Learning : Machine learning models are usually shallow (such as a simple decision tree or regression model). They usually have only one or two layers of complex structure and rely on artificially extracted features.

• Deep Learning : Deep learning models often have multiple hidden layers (hence the name "deep"). These hierarchical structures enable deep learning to automatically perform feature abstraction and extract more complex and abstract patterns.

Computing requirements

• Machine learning : Traditional machine learning algorithms generally do not require very high computing resources, can be run on ordinary computers, and are suitable for small or medium-sized data sets.

• Deep Learning : Deep learning models often require significant computing resources, especially during training. In order to train complex deep neural networks, it is usually necessary to use high-performance GPUs or TPUs (Tensor Processing Units) to accelerate calculations.

Data volume requirements

• Machine Learning : Machine learning models can work even with smaller amounts of data, especially if feature engineering is done well. When the amount of data is moderate, traditional machine learning methods can usually achieve better results.

• Deep learning : Deep learning models often require large amounts of labeled data to take advantage of them. If the amount of data is small, deep learning may not be as effective as traditional machine learning methods.

Application scenarios

• Machine learning : suitable for various common tasks, such as classification, regression, clustering, etc. Machine learning can be applied to many fields such as finance, medical care, recommendation systems, and market prediction.

• Deep learning : Deep learning is mainly used to process high-dimensional data, such as image recognition (such as face recognition, object detection), natural language processing (such as speech recognition, text generation), video analysis, automatic driving, etc.

learning style

• Machine learning : including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning, etc. Traditional machine learning algorithms rely more on manually designed features and labeled data.

• Deep Learning : Typically uses supervised, unsupervised, or self-supervised learning, but has the ability to learn on its own and extract features from large amounts of unlabeled data.

For example:

• Application of machine learning : Suppose you want to make a spam email classifier. Using traditional machine learning methods, you need to extract some manual features (such as the length of the email, keywords contained, etc.), and then use these features to train a classification model .

• Application of deep learning : For spam classification, deep learning models (such as convolutional neural networks or recurrent neural networks) can automatically learn and extract useful features directly from the original text data without manually designing features, which usually results in higher accuracy. accuracy.

Summarize

• Machine learning relies on the manual design of data and features, typically dealing with smaller-scale tasks.

• Deep learning automatically learns complex patterns in data and can process large-scale, high-dimensional data, making it suitable for tasks that require high computing resources.