Author: Zen and the Art of Computer Programming
1 Introduction
I haven’t done a technical blog for a long time. I recently discovered that I lack technical blogging skills and am interested in artificial intelligence and machine learning technologies in the industry. Learning from scratch takes a lot of time and energy. In order to reduce costs, we hope to collaborate on a free high-quality technical article. The content shared this time is "How Programmers Can Achieve Financial Freedom Series 1: Learn and Apply Machine Learning and Artificial Intelligence Technology". Everyone is welcome to participate in the co-construction in the comment area!
Programmers are a high-level profession, and mastering technical skills and the ability to solve practical problems are prerequisites. For technicians, the key to achieving financial freedom is to continuously learn, improve themselves, develop their own products and services, and create better value. Many technical staff are in the stage of seeking knowledge. They want to grow quickly or achieve success through technology. They have the mentality of "wanting to use some programming to solve practical problems." They need to use various news and tools to obtain real-time information, monitor and analyze technology, and continuously accumulate knowledge. This includes research in the fields of machine learning and artificial intelligence.
This article will use machine learning and artificial intelligence technologies to teach you how to learn and apply these technologies to solve practical problems. We will introduce some basic concepts and terminology, and then give detailed code examples and explanations based on algorithm principles and specific operation steps. Finally, future development directions and challenges will be given. I hope that by reading this article, it can help more programmers gain knowledge about machine learning and artificial intelligence and achieve financial freedom.
2. Background introduction
What is machine learning? What is artificial intelligence? How are they connected? What can machine learning be used for? Why should we learn and apply machine learning and artificial intelligence technologies?
2.1 Concept
2.1.1 Machine Learning
Machine Learning is a field of scientific research that enables computers to learn. It uses algorithms to simulate human learning behavior to complete task automation, thereby making the machine intelligent. It can process and analyze large amounts of data and automatically find patterns, trends and rules in the data, thereby predicting results or making decisions on the best actions for unknown input data. By collecting and analyzing hidden relationships in data, machine learning algorithms are able to provide solutions to complex problems or environments.
Machine learning algorithms are usually divided into three categories: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning means that the machine learning model obtains labels from the training data set, and then learns the inherent laws of the data. This law can be used to classify, regress, or predict new samples. Commonly used algorithms for supervised learning include linear regression, logistic regression, decision tree, random forest, support vector machine, K nearest neighbor, naive Bayes, Adaboost, GBDT, XgBoost, etc.
Unsupervised learning means that the machine learning model does not need the labels of the training data set and learns by analyzing, clustering or density estimation of the structure of the data set. Commonly used algorithms for unsupervised learning include K-means, DBSCAN, EM, spectral clustering, hierarchical clustering, association rules, PCA, t-SNE, etc.
Reinforcement learning refers to a machine learning model interacting with the environment to maximize rewards by learning the best strategy. Commonly used algorithms for reinforcement learning include Q-learning, A3C, DDPG, PPO, DQN, etc.
2.1.2 Artificial Intelligence
Artificial Intelligence (AI) is a technology that allows machines to think, communicate and learn like humans. Its core is to introduce computer vision, language understanding, reasoning and other capabilities into our daily lives. AI has penetrated into every aspect of our lives and can intelligently do various repetitive tasks, solve some specific problems, communicate with people, etc.
At present, artificial intelligence technology has entered a new era. It brings huge business value such as towing, delivery, chatbots, fraud identification, etc. At the same time, it has also produced a group of young talents, such as Turing Award winner Walter Pike, Alan Kurzweil, Kai-fu Lee, Li Hang, etc.
Generally speaking, artificial intelligence refers to technology that enables machines to behave like humans. It includes two aspects: cognition and computing, that is, how to make computers intelligent; how to make machine learning algorithms, optimization methods, neural networks, etc. work smoothly.
2.2 Contact
Artificial intelligence technology refers to the use of computer computing, storage, network, image processing, pattern recognition and other functions to enable machines to perform and understand tasks like humans. Machine learning is a technology that enables computers to learn. The two are closely linked, and because of this, some complex business problems can be solved. For example, we can complete image recognition tasks by learning images, extracting features, and then performing operations such as classification and recognition. If we can apply artificial intelligence, machine learning and other technologies within enterprises, we can help enterprises solve some practical and complex needs that value efficiency and speed, and improve management levels.
3. Basic concepts and terminology
3.1 Definition
- Data: refers to the data collection used for training, testing and prediction in the machine learning process. The data contains multiple dimensions such as features, labels, samples, text, audio, and video.
- Model: refers to the learning algorithm used by the machine learning system, which can predict or make decisions on the answer to a certain question based on data.
- Features: refers to the effective features used to represent the data obtained after abstracting and transforming the input data. It is an important basis for determining the learning process.
- Goal: refers to the purpose that the machine learning system wants to achieve. It is also a key indicator for evaluating the effect of the model and determining the quality of the model.
- Training set: refers to the data set used to train the model.
- Test set: refers to the data set used to test the performance of the model.
- Hyperparameters: refers to the parameters in the training process of the machine learning system model. It has nothing to do with the model itself and is the key to model selection.
- Overfitting: refers to the fact that the training error of the model continues to decrease, but the performance on the verification set drops significantly, and the model begins to underfit.
- Underfitting: It means that the training error of the model has always been maintained at a small value, but the performance on the validation set is much higher than the training set, and the model is severely underfitted.
- Training error: refers to the degree of error shown by the model on the training data set.
- Generalization error: refers to the degree of error shown by the model on the test data set. The generalization ability of the model can measure the robustness of the model.
- Model selection: refers to comparing the effects of different models on the same data and selecting the best model as the final prediction model.
- Standard deviation: indicates the degree of dispersion of the sample.
- Mean: Represents the weighted average of sample values.
- Variance: A statistic that measures the dispersion of a sample. The smaller it is, the closer the sample set is to a normal distribution.
- Distribution: The probability density function or probability accumulation function of a sample.
- Confusion matrix: refers to a matrix used to evaluate the quality of a classification model. It mainly displays the number of real samples and predicted samples.
- Accuracy: refers to the proportion of correctly predicted samples to all predictions. The higher the accuracy, the higher the accuracy of the model.
- Recall rate: refers to the proportion of correctly predicted samples among all samples. The higher the recall rate, the higher the recall rate of the model during retrieval.
- F1 value: An evaluation index that comprehensively considers accuracy and recall. The larger the value, the better the performance of the model.
- Generalized least squares method: It is a machine learning method that assumes that the model is a linear model determined by a parameter vector, which fits the training data by minimizing the squared error.
- Bayesian estimation: It is a statistical method based on probability theory that derives estimates of model parameters through known sample data and its corresponding probability distribution.
- Maximum likelihood estimation: It is an estimation method that obtains model parameters by maximizing the likelihood function of the training data.
- Ensemble learning: It is a combination of multiple learners that can overcome the limitations of a single learner and improve the performance of the model.
- Random Forest: It is an ensemble learning method that builds a highly accurate classifier through the combination of multiple decision trees.
- GBDT: It is one of the gradient boosting methods. It is an integrated learning framework based on a combination of base classifiers.
- XgBoost: It is one of the gradient boosting methods. It is a boosting framework added in a tree structure.
- TensorFlow: Google’s open source deep learning framework.
- PyTorch: Facebook’s open source deep learning framework.
- Keras: A deep learning API that can easily and quickly build models and train them.
- Data enhancement: refers to performing various transformations on original data to generate new data to expand the size of the original data set.
- Early Stopping: During the iterative training process, the current round of training is stopped early based on the model's performance on the validation set to avoid overfitting.
- Overfitting: refers to the model learning the noise in the training data, resulting in poor performance on the test data.
- PCA: A commonly used unsupervised dimensionality reduction method that maps original features into a new low-dimensional space by analyzing the correlation between features.
- t-SNE: A method for visualizing high-dimensional data that highlights key points and edges by embedding the high-dimensional data in a low-dimensional space.
- ReLU activation function: It is an activation function and an approximation of a nonlinear function, which can effectively prevent the gradient from disappearing or exploding.
- Softmax function: It is another activation function, usually used for multi-classification problems. It converts the output of each classification into the range of (0,1), and the sum is equal to 1.
- SVM: Support vector machine is a two-category classification model, which is a supervised learning method. It divides each sample into different categories by finding the segmentation hyperplane with the largest feature vector projection distance.
- KNN: K-Nearest Neighbors, the K nearest neighbor algorithm is an unsupervised learning algorithm that predicts its classification label by analyzing adjacent samples.
- Decision Tree: A basic classification and regression method used to build classification or regression trees.
- Random Forest: An integrated learning method that implements the random forest algorithm through the combination of multiple decision trees.
- Gradient Boosting: Gradient Boosting is a machine learning method that uses the residuals of the prediction results of the previous model to build a new model.
- Logistic Regression: A commonly used binary classification model, which is a supervised learning method and is used to predict the probability of each event.
- EM algorithm: Expectation-Maximization Algorithm, is an iterative algorithm used to estimate the prior probability distribution and parameters of latent variables to maximize the likelihood function.
- AUC: Area Under the Curve, the area under the ROC curve, is used to evaluate the performance of the binary classification model.
- ROC curve: Reception rate-recall rate curve, used to describe the quality of the model.
- F1 Score: F1 Score = 2 * (precision * recall) / (precision + recall), which is also an indicator of model performance.
- Gradient Descent: is an optimization algorithm used to find the extreme value of a function.
- Batch Normalization: It is a data preprocessing method that normalizes the data to make the distribution of each sample consistent and speed up the convergence speed.
- Dropout: is a regularization method that prevents overfitting by discarding some connections of the neural network.
- LSTM: Long Short Term Memory, long short-term memory neural network, a special RNN that can capture sequence information.
- GPU: Graphics Processing Unit, graphics processing chip, is a chip specialized in processing high-speed computing tasks.
- CUDA: Compute Unified Device Architecture, a unified computing device architecture, is a protocol specification for GPU computing technology.
- CPU: Central Processing Unit, central processing unit, is the core computing component of the computer.