Nine chapters of algorithms: deep learning, reinforcement learning, machine learning, recommendation systems, image processing, text processing, sequence processing, search engines, data analysis, etc.

Author: Zen and the Art of Computer Programming

1 Introduction

With the rapid development of computer technology, the fields of artificial intelligence and machine learning have ushered in an era of vigorous development. From "knowledge graphs" to "retail system automation", artificial intelligence technology is changing all aspects of social life. Traditional artificial intelligence technologies rely on complex computing capabilities on hardware, such as neural networks, decision trees, etc. However, for actual production scenarios, these methods cannot meet the needs due to limitations in engineering volume, cost, reliability, etc. In January 2017, Baidu open sourced an AI development toolkit based on the TensorFlow framework, called PaddlePaddle. This toolkit provides a variety of advanced API interfaces, supports mainstream machine learning algorithms, and is emerging in the field of artificial intelligence.

In the current artificial intelligence boom, there are huge challenges whether in the fields of product research and development, e-commerce search sorting, government intelligent assistants or financial big data analysis. How to use artificial intelligence technology to solve problems has become an issue for more and more companies. issues to consider. This resulted in the "Jiuzhang Algorithm", an open source AI platform.

This column elaborates on the "Nine Chapter Algorithm" from 9 aspects, including deep learning, reinforcement learning, machine learning, recommendation systems, image processing, text processing, sequence processing, search engines, data analysis and other algorithms to help Readers quickly master artificial intelligence technology. At the same time, it also guides readers to understand the application scenarios and advantages of the "Nine Chapter Algorithm" based on actual cases.

2. Background introduction

AI empowered industries

In recent years, with the rapid development of science and technology, the new generation of artificial intelligence technology has gradually been adopted by all walks of life. With the continuous progress and breakthroughs of artificial intelligence technology, especially the introduction of machine learning and deep learning technology, artificial intelligence technology has been widely used in many innovative industries, such as:

Manufacturing industry : automated production, image recognition, object detection, intelligent maintenance, robot collaboration, etc., can improve product quality, reduce costs, and improve work efficiency;
Medical and health fields : imaging diagnosis, precision medicine, real-time prevention, personal care, etc., can help patients get treatment faster and improve their health status;
Social field : Sentiment analysis, news push, Q&A community, feedback, etc. based on big data can help users better communicate and interact with others;
Financial and accounting fields : risk control, risk assessment, credit scoring, fraud monitoring, etc., which can help institutions better manage economic resources;
Game industry : Artificial intelligence makes games more vivid, exciting and challenging;
Internet field : personalized recommendations, advertising, search rankings, etc., can help users obtain content and services that better meet their needs.

In addition to innovative enterprise applications, with the popularization and application of artificial intelligence technology, some traditional industries are also facing the challenge of the "artificial intelligence revolution". For example, in the field of e-commerce, e-commerce platforms represented by Amazon increasingly rely on automated decision-making and product recommendation functions, prompting merchants to turn their attention to the emerging personalized e-commerce field; in the field of finance and insurance, the use of artificial intelligence technology Applications can help customers achieve accurate pricing and optimize insurance approval efficiency; in the transportation fields such as aviation and railways, navigation systems based on video and map information are being widely used, and artificial intelligence-related technologies will play a more important role in route planning and early warning systems. big effect.

The concept of "Nine Chapter Algorithm"

Open source AI platform - "Nine Chapters Algorithm", referred to as "Nine Chapters Algorithm". "Jiuzhang Algorithm" is a software platform that can implement various artificial intelligence technologies, including deep learning, reinforcement learning, machine learning, recommendation systems, image processing, text processing, sequence processing, search engines, data analysis and other directions. algorithm. Currently, "Nine Chapters Algorithm" has been used in the business of many well-known companies.

deep learning

Deep Learning is a machine learning method for computer vision, speech, language and other fields. It is a statistical learning method based on a neural network structure. The model it trains can understand the original input end-to-end. data characteristics and generate corresponding output results. Its main feature is to use multi-layer (layer depth can reach dozens or even hundreds of layers) nonlinear mapping functions to map the input data to a higher dimensional space, and then predict the output through a nonlinear regression algorithm. Deep learning has excellent performance in solving image recognition, speech recognition, video analysis and other fields, and has achieved a very high success rate.

The main components of deep learning include:

Neural network: Based on the back propagation algorithm, it is composed of layers of neuron connections. It is highly nonlinear and can simulate the human neural network structure;
Activation function: The output value of each neuron is weighted and summed by the activation function. The function of the activation function is to introduce nonlinear factors;
Loss function: measures the difference between the neural network’s predicted value and the true value, and determines the training goal of the model.

The main advantages of deep learning are:

Strong generalization ability: Deep learning algorithms can fit complex functional relationships on the same structure by increasing the number of hidden layer nodes or using a larger range of training data, and have strong generalization ability;
Simple model: Deep learning algorithms can avoid overfitting problems by reducing the number of parameters, and the model is simpler than other machine learning methods;
Fast speed: The high-speed computing performance of deep learning algorithms under conditions of large amounts of data and high computing power can complete model training and prediction tasks faster.

reinforcement learning

Reinforcement Learning means that in a given environment, when an agent takes actions, it needs to continuously explore the environment, learn new knowledge, and find optimal strategies to maximize rewards. In this way, the agent continues trial and error, constantly updates its strategy, and finally obtains a good strategy.

The main components of reinforcement learning include:

Environment: refers to the interaction process between an intelligent agent and the external world. It generally has three elements: state, action, and reward;
Action: The behavior that the agent takes based on the current state. The action space defines all the action options that the agent may take;
Reward: A signal that reflects the quality of the agent's behavior. It can directly affect the agent's benefits, that is, the reward at each step defines the state distribution of the next step;
Status: reflects the current environmental state of the agent and defines the environmental changes that the agent can perceive.

The application scenarios of reinforcement learning mainly include:

Autonomous driving: Without human participation, machines automatically guide vehicles to their destinations, improving the safety and accuracy of human-driven cars;
Virtual reality: realizes the interaction between virtual reality and actual scenes. People can interact with the virtual environment by operating virtual objects, thereby realizing the application of virtual reality;
Recommendation system: Provide consumers with more personalized and relevant shopping suggestions, help consumers achieve product selection, purchase decisions, browsing preferences, etc., improve efficiency and increase profits;
Game theory: A variety of game games are implemented, such as chess, poker, Go, etc. The agent continuously learns and optimizes strategies during the game process, and has a higher probability of winning;

machine learning

Machine Learning is a science that studies how computers can simulate or discover the behavior of complex systems such as nature, economy, and finance, and use these experiences to improve their own efficiency and build predictive models. The purpose of machine learning is to establish a model that can predict known data, and to analyze, make decisions or improve unknown data based on this model. Machine learning can be used in many fields such as classification, clustering, regression, pattern recognition, decision trees, random forests, and Bayesian networks.

The main components of machine learning include:

Data: Machine learning algorithms rely on a large amount of training data for training. The data usually includes features (Input) and labels (Output). Features determine the input to the model, and labels are the results to be predicted;
Model: A model is a function that accepts features as input and outputs predictions. Different models have different expression forms for different tasks, such as logistic regression, support vector machine, neural network, etc.;
Algorithm: Machine learning algorithm refers to the calculation rules used to train models and adjust parameters. There are different advantages and disadvantages between different algorithms, including naive Bayes, decision tree, neural network, etc.

The main application scenarios of machine learning include:

Image recognition: Through images captured by cameras or scanners, machine learning algorithms can identify and mark objects and elements in images, helping enterprises manage, analyze, and monitor large amounts of images and videos;
Text classification: By classifying, summarizing and labeling large amounts of text data, machine learning algorithms can help companies automatically classify massive amounts of text and improve the accuracy and efficiency of search results;
Spam filtering: Through machine learning algorithms, the mail server can automatically determine whether newly received emails are spam, effectively reducing the pressure of manual screening and improving work efficiency;
Face recognition: Through photos or videos captured by cameras or cameras, machine learning algorithms can identify face information, thereby achieving face tracking, identification and verification.

Recommended system

Recommendation System refers to the technology that recommends new products or services to users based on their historical behavior, interests, taste preferences, location information, etc. It is the infrastructure for building recommendation engines for products, clothing, pictures, etc. The recommendation system recommends related products or services by analyzing the user's purchasing habits, preferences, interests, personal information, brand preferences, etc. The application scenarios of recommendation systems include e-commerce websites, social networks, Weibo, news, encyclopedias, music, videos, movies, books, etc.

The main components of the recommendation system include:

User portrait: The recommendation system needs to analyze the user's behavioral habits and build user portraits to make fine-grained personalized recommendations for different user groups;
Recall mechanism: The recommendation system needs to recall based on user portraits and selectively display important and potentially interesting products or services;
Sorting algorithm: The recommendation system needs to sort the recommended results according to the user's preferences, commercial value, etc., and select the product or service that is most suitable for the user;
Personalized modeling: The recommendation system needs to train a personalized model based on the user's historical behavior, interests and hobbies, and establish the contextual characteristics of the recommendation results.

The application scenarios of recommendation systems mainly include:

E-commerce websites: Through recommendation algorithms, e-commerce websites can accurately meet users’ shopping needs, improve user stickiness, and help companies save marketing costs;
Music recommendation: Through the recommendation algorithm, the music player can automatically recommend suitable songs for users to improve the user’s listening experience;
Search recommendation: Through the recommendation algorithm, the search engine can make personalized recommendations based on the user's search history, interests and hobbies, device information, etc., to improve the user's retrieval efficiency.

Image Processing

Image processing (Image Processing) refers to various image processing technologies for digital images, including image enhancement, image repair, image registration, image defogging, super-resolution, image description, image segmentation, image identification, image compression, image Retrieval, image matching, etc.

The main components of image processing include:

Segmentation and classification: Image processing requires segmentation and classification of images, that is, dividing images into multiple areas or different types. Image segmentation is the basis for image analysis, image retrieval and image recognition;
Dehazing and image enhancement: Image processing requires dehazing and enhancing images, that is, eliminating noise, brightening, sharpening, reducing, etc. Image dehazing is a prerequisite for image enhancement;
Matching and features: Image processing requires feature extraction and matching, that is, identifying features in images and matching the differences between two images. Image matching is the basis of image processing.

The main application scenarios of image processing include:

Face recognition: The application of image processing technology can help enterprises quickly locate faces in massive images, implement face database retrieval, authentication, registration, verification and other functions to improve efficiency;
License plate recognition: On crowded roads such as highways, train tracks and viaducts, license plates need to be recognized. The application of image processing technology can improve the accuracy, efficiency and precision of recognition;
Image restoration: Traditional image restoration technology often requires manual processing, while image processing algorithms can automatically identify and process defects in images and improve the overall quality of the image.

text processing

Text Processing refers to various text processing technologies for text, including text segmentation, part-of-speech tagging, named entity recognition, keyword extraction, topic models, sentiment analysis, text summarization, comment mining, language models, etc.

The main components of text processing include:

Word segmentation and part-of-speech tagging: Text processing requires word segmentation and part-of-speech tagging of the text, that is, dividing sentences, documents, etc. into words, stems, parts of speech and other elements to facilitate subsequent analysis and processing;
Named entity recognition: Text processing needs to identify named entities in the text, such as person names, place names, organization names, etc., to help subsequent semantic understanding and extraction;
Keyword extraction: Text processing requires extracting keywords from the text, such as paper titles, questions, opinions, authors, etc., to help subsequent searches and information retrieval;
Sentiment analysis: Text processing requires sentiment analysis of the text, such as evaluating positive and negative, comparing the emotional tendencies of two paragraphs of text, judging the quality of the text, etc., to help subsequent public opinion analysis and recommendation systems.

The main application scenarios of text processing include:

Spam filtering: The application of text processing technology can help enterprises filter and clean spam, effectively reducing processing volume and shortening response time;
Text classification: The application of text classification algorithms can help enterprises automatically classify massive amounts of text and improve the accuracy, efficiency and precision of search results;
Intelligent customer service: The application of intelligent customer service system can help users communicate directly with customer service employees, improve service quality and reduce service costs.

sequence processing

Sequence Processing refers to various sequence processing technologies for text, image and other sequence data, including sequence feature extraction, sequence clustering, sequence reconstruction, anomaly detection, time series prediction, etc.

The main components of sequence processing include:

Time series characteristics: Sequence processing needs to extract the time characteristics of the data, including time series trends, cycle periods, fluctuation amplitudes, etc., to facilitate subsequent analysis and processing;
Clustering: Sequence processing requires clustering data to find hidden patterns and connections in the data to help subsequent classification and prediction;
Anomaly detection: Sequence processing requires anomaly detection of data to find abnormal points in the data to help subsequent monitoring and prediction;
Prediction and recommendation: Sequence processing requires prediction of unknown data to help with subsequent recommendations, searches, risk control, etc.

The application scenarios of sequence processing mainly include:

Stock market analysis: The fluctuations of the stock market have time series characteristics. Through the method of sequence processing, stock price trends and fluctuation trends can be analyzed to help investors control risks and positions;
Weather forecast: Weather forecast data has time series characteristics. Through the sequence processing method, periodicity, jumps, etc. in the data can be discovered to help users guide daily life and monitor public opinion;
Network traffic prediction: Network traffic data has time series characteristics. Through the sequence processing method, abnormal behaviors such as network attacks and crawlers can be discovered, helping operators conduct traffic control, security detection, etc.

search engine

Search Engine refers to a tool for network retrieval and information collection, which can realize full-text search, information retrieval, web page indexing, text classification, association rules, link analysis and other functions. The application scenarios of search engines include search boxes, query suggestions, web page rankings, web page embedding, search log analysis, etc.

The main components of the search engine include:

Retrieval component: The search engine needs to provide a retrieval component to implement the query request submitted by the user and retrieve the corresponding documents;
Index component: Search engines need to provide an index component to store documents and their keywords, abstracts, URL addresses and other information to facilitate retrieval;
Query analysis component: Search engines need to provide query analysis components to analyze user query requests, sort them by relevance, and return the best query results;
Result presentation component: The search engine needs to provide a result presentation component to present the retrieved documents to the user.

The application scenarios of search engines mainly include:

Web search: Search engine applications can provide users with search services based on massive web pages, improving user query efficiency and experience;
Vertical field search: Search engine applications can provide more convenient search services for users in specific industries, such as news, stocks, knowledge bases, etc.;
Search recommendations: Search engine applications can provide users with personalized search result recommendations to improve users’ search satisfaction.

data analysis

Data Analysis refers to statistical analysis, data mining, prediction model design, image processing, etc. on various data. The application scenarios of data analysis include data visualization, analysis reports, business decision-making, knowledge discovery, risk control, security monitoring, etc.

The main components of data analysis include:

Data collection and cleaning: Data analysis requires collecting, processing, analyzing and cleaning data, unifying data formats, deleting duplicate records, etc.;
Data conversion and standardization: Data analysis requires conversion and standardization of data, and adjustment of relationships, formats, units, etc. between data;
Data modeling and analysis: Data analysis requires the use of machine learning, deep learning and other algorithms to model and analyze data and obtain results;
Data visualization: Data analysis requires visualizing data to facilitate viewing, understanding, and analysis.

The application scenarios of data analysis mainly include:

Financial big data analysis: The application of financial big data analysis can help institutions better manage assets and markets, improve efficiency, and reduce risks;
Internet data analysis: The acquisition, analysis, processing, storage and other aspects of Internet data are all inseparable from the means of data analysis;
Image recognition and analysis: The acquisition, analysis, processing, storage and other aspects of image data are all inseparable from the means of data analysis.

3. Explanation of basic concepts and terms

（1）TensorFlow

TensorFlow is an open source machine learning library launched by Google. The first version was released in November 2015 and is well received by the industry. Its core concept is Computational Graph, which is a high-order way to describe mathematical calculations. It was originally pioneered by colleagues and open sourced in June 2011. In November 2015, TensorFlow announced the completion of version 1.0 of TensorFlow, which was jointly developed by Stanford University Washington, Google Brain Lab, and other teams within Google, making it open source. TensorFlow version 1.0 is the first version of TensorFlow and the initial version of the TensorFlow project. It provides high-order mathematical operation functions and a variety of deep learning models. It has powerful capabilities and can handle huge data and complex models.

TensorFlow's core programming interface is the C++ language API, and supports bindings in multiple languages such as Python, Java, Go, and JavaScript. In addition, it has other kinds of bindings, such as Scala, Swift, Ruby, R, etc.

Features provided by TensorFlow include:

Supports dynamic calculation graphs, you can easily construct calculation graphs and run the solution process;
Provides a large number of machine learning models, including linear models, convolutional neural networks, recurrent neural networks, recurrent neural networks, deep belief networks, etc.;
Can be connected with other tools, including numpy, pandas, matplotlib, scikit-learn, etc.

TensorFlow can run on various platforms, including Linux, Windows, MacOS, etc., and supports CPU and GPU computing.

（2）Hard

Keras is an advanced, user-friendly artificial intelligence neural network API that can be used to quickly build and train neural networks. It supports neural networks with multiple model architectures, capable of supporting various types of layers, such as Dense, Activation, Conv2D, MaxPooling2D, Dropout, etc. It is compatible with TensorFlow and can directly use TensorFlow's calculation graph for training.

Keras is great for beginners because it allows users to write concise code to create, train, and test neural networks. It also provides other functions such as automatic gradient finding, data normalization, saving and loading models, etc. Keras is ideal for researchers and developers because it provides a convenient interface to quickly build and test neural networks.

Keras can be installed using the pip command or compiled and installed from source code. Keras can be used with TensorFlow and Theano as well as other tools such as scikit-learn, Matplotlib, Pandas, Numpy.

（3）PyTorch

PyTorch is a Python-based machine learning library open sourced by Facebook AI Research. Version 1.0 was released in January 2017. It is a deep learning library based on the Torch framework, which has higher flexibility, speed and ease of use than TensorFlow. PyTorch provides high flexibility in model definition, training, evaluation, and inference, and is easier to use.

PyTorch has the following features:

PyTorch uses automatic differentiation to automatically calculate gradients, so complex neural networks can be implemented with less code;
PyTorch has parallel computing capabilities on GPU and CPU, and can easily handle large-scale data sets;
PyTorch provides powerful tools, such as Torchvision, Torchtext, Torchaudio, etc., which can easily perform data loading, preprocessing, processing and other operations;
PyTorch is highly scalable, and models can be easily customized through custom functions, modules, parameter initialization, etc.

PyTorch can be installed using the pip command or compiled and installed from source code. PyTorch can only run on CUDA and CPU, and cannot be used with TensorFlow.