This blog covers several important aspects of machine learning and deep learning, explained in detail across multiple chapters. First, it introduces the basic types of machine learning questions and the distribution of scores, emphasizing the algorithms and theories that need to be mastered in the course, including stochastic gradient descent, regularization methods (such as L1 and L2), and Dropout. Next, it delves into the applications of Logistic regression, Softmax regression, and their loss functions, and explains the difference between empirical risk and structural risk. Subsequently, the blog analyzes the advantages and disadvantages of several common activation functions (such as Sigmoid and ReLU) and the basics of feedforward neural networks. In the convolutional neural network (CNN) section, it describes in detail the characteristics of CNNs, the roles of convolutional layers and pooling layers, and the working principles of residual networks. Following this, the blog introduces recurrent neural networks (RNNs) and their variants LSTM and GRU, discussing their advantages and disadvantages in handling sequential data. The network optimization chapter focuses on the improvement of learning rates, adaptive adjustment methods (such as Adagrad, Adadelta, RMSProp), gradient optimization techniques (such as momentum method, Nesterov accelerated gradient, Adam algorithm), and data normalization methods. The attention mechanism section introduces the significance, formulas, and processing flow of Attention and Self-Attention, as well as the structure and advantages of Transformers. Finally, the blog explores the basic elements and common algorithms of reinforcement learning, such as policy iteration, value iteration, SARSA algorithm, and Q-Learning algorithm, and explains the state value function and action value function in reinforcement learning through Monte Carlo sampling methods. Through this content, readers can comprehensively understand the core concepts and technical applications of machine learning and deep learning.

This blog first introduces the basic concepts of machine learning, including task types such as regression, classification, clustering, and dimensionality reduction, as well as learning methods such as supervised, unsupervised, and reinforcement learning. Then, it focuses on model evaluation and selection, introducing evaluation methods such as hold-out, cross-validation, and bootstrap, and demonstrates how to implement these methods through code examples. In terms of performance metrics, the article introduces indicators such as mean squared error (MSE) and mean absolute error (MAE), and explores the application of ROC curves. In model selection, the distinction between empirical error and generalization error is emphasized, pointing out that the smaller the generalization error, the better, but the empirical error is not necessarily optimal. The article also discusses the relationship between bias and variance, indicating that during training, it usually shifts from bias-dominated to variance-dominated. The linear model section introduces methods such as least squares and logistic regression, and optimizes model parameters through gradient descent. The neural network chapter focuses on perceptrons and backpropagation algorithms, introducing how to address overfitting issues through early stopping and regularization. In the support vector machine section, the article explains in detail the principles of linearly separable SVM, linear SVM, and nonlinear SVM, and solves nonlinear classification problems through kernel tricks. Finally, the ensemble learning section introduces Adaboost and Bagging algorithms, exploring methods to enhance diversity through data sample perturbation, input attribute perturbation, output representation perturbation, and algorithm parameter perturbation. Through these contents, readers can comprehensively understand the basic theory and practical methods of machine learning, mastering how to evaluate and optimize model performance.

When using PyInstaller to package client applications, you may encounter some cross-platform issues. Firstly, Python 3.9 and above no longer support Windows 7, so if you need to run on Windows 7, the Python environment should be limited to version 3.8. Secondly, there may be missing DLL files, such as api-ms-win-core-path-l1-1-0.dll, which is usually because PyInstaller did not include all dynamically loaded libraries during packaging. The solution is to place the missing DLL files in the program’s root directory or system directory. Additionally, parts of the Qt library’s dynamic loading may be missing, leading to ImportError errors. Solutions include copying Sitepackages/QtGui from the environment to the program’s root directory or using the hidden-import option to manually load missing modules during packaging. Finally, if PyInstaller cannot obtain the source code, you can manually copy the missing environment packages to the program’s root directory by checking the error log, or specify hidden-import to import these packages during packaging. These methods can effectively solve the issues encountered when packaging applications with PyInstaller on different platforms.

This blog provides a detailed introduction to the basic concepts of parallel computing and the application of CUDA programming. First, it discusses the difference between concurrency and parallelism, the differences between serial and parallel computing, and the classification methods of parallel computing, including computational models, program logic, and application perspectives. Then, it introduces Flynn’s taxonomy, explaining the differences between SISD, SIMD, MISD, and MIMD. Next, it delves into Amdahl’s Law, analyzing its role in improving program performance, and demonstrates how to apply Amdahl’s Law to calculate speedup and the number of processors needed through specific examples. In the CUDA programming section, the heterogeneous computing model is introduced, analyzing the differences between CPU and GPU, and emphasizing the advantages of GPU in parallel computing. It discusses the organization of CUDA threads, including the concepts of threads, thread blocks, grids, and kernel functions. It further explains the CUDA host/device programming model, describing the role of different function qualifiers and the limitations of CUDA kernel functions. Additionally, the blog introduces the SIMT parallel computing model, the streaming multiprocessor SM in GPU architecture, the memory model, and memory access patterns, emphasizing the use of shared memory and solutions to bank conflicts. Example code demonstrates how to implement image flipping, array addition, matrix transposition, square matrix multiplication, histogram calculation, reduction summation, and the TOP K problem in CUDA. Finally, the blog provides experimental guidance, introducing three methods for calculating PI, the implementation of thread pools, optimization methods for matrix multiplication and transposition, and helps readers consolidate their knowledge through the analysis of real exam questions.

This article accelerates the solution of large-scale problems using GPU multi-core by writing CUDA kernel functions, taking matrix transposition and other 5 issues as examples. First, it introduces the algorithm process of matrix transposition, including storing the matrix in GPU memory, defining CUDA kernel functions, calling kernel functions to execute transposition, and copying the results back to host memory. In the code implementation, shared memory is used to optimize access to global memory, avoiding bank conflicts. Then, the design of the kernel function for matrix multiplication is discussed, emphasizing the use of shared memory and non-square matrix processing. The code example shows how to reduce global memory access through shared memory to improve performance. Next, the implementation of histogram statistics is introduced, using shared memory and atomic operations to improve efficiency. The reduction summation section proposes optimization strategies of cross-grid looping and interleaved pairing. Finally, the solution to the TOP K problem is explored, using CUDA reduction to efficiently sort and select the top K elements. Through these examples, this article demonstrates how to use CUDA technology to accelerate large-scale data processing and matrix operations.

‘Bayes’ theorem is a tool for inferring unknown events through known probabilities, widely used in the Bayesian school and machine learning’s Naive Bayes classifier. The Bayesian school believes that nothing is random; randomness is just a manifestation of insufficient information. Through Bayes’ theorem, we can infer using various probabilities before an event occurs after the event has happened. In the formula, $P(c|x)$ represents the posterior probability, which is the probability of an event occurring given certain conditions, while $P(c)$ is the prior probability, the original probability before the event occurs. $P(x|c)$ is the likelihood, representing the probability of observing x under condition c. $P(x)$ is a normalization constant used to ensure the total probability sums to 1. In machine learning, Bayes’ theorem defines the Naive Bayes classifier, helping models learn the distribution of data through maximum likelihood estimation. Prior and posterior probabilities are two core concepts in Bayes’ theorem. The prior probability is derived from past data and experience, while the posterior probability is the probability of inferring the cause based on the result after the event has occurred. The calculation of the posterior probability depends on the prior probability; without the prior probability, the posterior probability cannot be calculated. The relationship between Bayes’ theorem and the law of total probability is that the law of total probability is cause-to-effect, while Bayes’ theorem is effect-to-cause. The law of total probability summarizes the probability of an event caused by several factors, while Bayes’ theorem is used to calculate the probability of each factor causing the result under known results. Through these formulas, we can make reasonable inferences and decisions in an uncertain world.’