This course is a new series of video tutorials on artificial intelligence and deep learning launched by Zhengdian Atom in 2023!
Total of 30 lessons9 hours and 42 minutes and 21 seconds
ENVI (The Environment for Visualizing Images) is a complete remote sensing image processing platform. The software processing technology in the application collection covers image data input/output, image calibration, image enhancement, correction, orthorectification, mosaic, and data fusion. As well as various transformations, information extraction, image classification, knowledge-based decision tree classification, integration with GIS, DEM and terrain information extraction, radar data processing, and three-dimensional display analysis.
Total of 49 lessons20 hours and 20 minutes and 47 seconds
The "Big Data Machine Learning" course is a basic theoretical course for senior undergraduates or graduate students in the information discipline. The purpose is to cultivate students to have an in-depth understanding of the theoretical basis of big data machine learning, a firm grasp of big data machine learning methods, and the ability to solve practical problems. and other comprehensive abilities. The main contents of the course include: basic theories of statistical learning, basic methods of machine learning, and deep learning theories and methods.
Total of 113 lessons15 hours and 39 minutes and 33 seconds
A data structure refers to a collection of data elements that have one or more specific relationships with each other. Under normal circumstances, carefully selected data structures can bring higher operating or storage efficiency. Data structures are often related to efficient retrieval algorithms and indexing technologies. This tutorial is from the shallower to the deeper and is super detailed. It is suitable for self-study, classroom teaching and after-class review and examination. Shanghai Jiao Tong University's algorithm and data structure video tutorial is taught by teacher Tan Xiaohong. Basically, no key points and difficulties are missed. The main contents of this course include: basic concepts related to data structure, basic operations and applications of linear tables, and the definition and application of stacks and queues. Basic operations, string pattern matching algorithms, basic concepts and algorithm implementations related to binary trees, graph storage structures and algorithms, and various search and sorting algorithms, etc.
Total of 29 lessons1 days and 10 hours and 20 seconds
"Image Processing and Analysis" is intended to enrich students' knowledge of image processing and analysis, and cultivate students' interest in learning, innovative thinking and practical ability in image processing and analysis. The purpose is to allow students to deeply understand the concept of image processing, master the methods and skills of image processing and analysis, and understand the development and application of digital image processing.
Total of 41 lessons6 hours and 4 minutes and 9 seconds
This course is a youth AI self-improvement project - computer vision course. The main organizers, speakers, and participants are all students, solving practical problems for students: bridging the gap between "technical novices" and "teachers think you understand" The gap between "textbook" and "practice" is bridged, and the problem that general academic articles/resources are difficult to read is solved. This course mainly consists of 8 lectures and 1 transformation challenge task. The lectures are held every other week. Each lecture will use easy-to-understand language to guide everyone to master AI-related knowledge points, including AI bird's-eye view and advanced guide, introduction to machine learning, classic neural networks, deep neural networks, convolutional neural networks, classification tasks, and detection tasks. , examples and parameter adjustment methods, and finally lead everyone to practice through transformation challenge tasks, etc.
Total of 43 lessons7 hours and 18 minutes and 59 seconds
The goal of machine learning is to program a computer to solve a given problem using sample data or past experience. There have been many successful applications of machine learning, including analyzing past sales data to predict customer behavior, face recognition or speech recognition, optimizing robot behavior to use the least amount of resources to complete a task, and various systems for extracting knowledge from biometric data. . In order to provide a unified discussion on machine learning problem solving, "Introduction to Machine Learning" discusses machine learning in statistics, pattern recognition, and neural networks. Applications in different fields such as artificial intelligence, signal processing, control and data mining.
Total of 42 lessons1 days and 4 hours and 6 minutes and 25 seconds
(1) Understand machine learning and introduce classic algorithms by introducing the basic problems of machine learning (classification, clustering, regression, dimensionality reduction); (2) Python third-party library sklearn (scikit-learn), explain how to apply machine learning algorithms to quickly solve problems approach to practical problems.
Total of 27 lessons3 hours and 17 minutes and 52 seconds
Through interesting animations, we will take you to get started with the robot operating system ROS from scratch.
Total of 69 lessons10 hours and 47 minutes and 47 seconds
Total of 6 lessons14 hours and 6 minutes and 10 seconds
Genetic Algorithms and Applications Yang Xiangbo, South China Normal University
Total of 8 lessons2 hours and 59 minutes and 24 seconds
Total of 61 lessons1 days and 3 hours and 16 minutes and 34 seconds
Learn myRIO from me
Total of 14 lessons2 hours and 17 minutes and 18 seconds
Digital image processing matlab version Shandong University open course
Total of 57 lessons22 hours and 4 minutes and 42 seconds
This course completely covers all core technologies in the field of data mining, including data preprocessing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computing, etc. Emphasizing on finding the best balance between the breadth, depth and interest of knowledge, the core ideas and key technologies of data mining, as well as some important knowledge points rarely covered in other related courses and textbooks, are described in vivid humor. This course is suitable for students from various majors and engineering and technical personnel who are interested in big data and data science. It does not pursue pure theoretical derivation, but organically combines theory with practice, allowing students to learn living knowledge, useful knowledge and real Your own knowledge, especially research methods and ways of thinking in the field of data analysis.
Total of 65 lessons14 hours and 55 minutes and 52 seconds
This course is an entry-level artificial intelligence course, suitable for beginners, and can help beginners learn artificial intelligence from scratch. This course adopts the professional textbook "Introduction to Artificial Intelligence" (4th Edition) compiled by Professor Wang Wanliang of Zhejiang University of Technology. It closely focuses on the basic ideas, basic theories, basic methods and applications of artificial intelligence, and integrates some cutting-edge aspects of artificial intelligence. content. This course has 12 lectures in total, including: overview of artificial intelligence, first-order predicate logic representation, production representation and frame representation, reasoning methods based on predicate logic, credibility methods and evidence theory, fuzzy reasoning methods, search and solution strategies , Genetic Algorithm and its Application, Ant Colony Algorithm and its Application, Expert System and Machine Learning, BP Neural Network and its Application and Hopfield Neural Network and its Application.
Total of 80 lessons12 hours and 15 minutes and 33 seconds
The course of Big Data Algorithms aims to teach students some basic algorithm design ideas on big data, including probabilistic algorithms, I/O efficient algorithms and parallel algorithms, so that students can be exposed to algorithm design and analysis that are different from traditional algorithm courses. ideas, and is guided by the latest research results, so that students participating in this course can understand the cutting-edge knowledge of big data algorithms. Through the study of this course, students can master the basic ideas of big data algorithm design, and through the homework of this course, master the technology of big data algorithm design and analysis.
Total of 35 lessons7 hours and 35 minutes and 52 seconds
The topic of this course is artificial neural networks and their applications. This article discusses the basic units of artificial neural networks, network structures, several commonly used artificial neural network algorithms and their applications in power systems.
Total of 4 lessons1 hours and 26 minutes and 3 seconds
Introduces basic algorithms, theories and practical tools that machine learning users in various fields should know.
Total of 65 lessons15 hours and 29 minutes and 53 seconds
Main content: definition of artificial intelligence, tree search algorithm, information-free search strategy, heuristic search strategy, constraint satisfaction problem solving, game algorithm, Bayesian network, hidden Markov model, Kalman filter. Features: The teaching time of artificial intelligence courses in the school is 32 hours. It is a required professional course for master's students in computer science and technology. Since the basic theory of artificial intelligence involves intelligent search, reasoning, machine learning, etc., it is an essential theoretical foundation for various research directions of current information graduate students, and can lay a good foundation for students to conduct in-depth research in various directions. The ideas can be applied to pattern recognition, intelligent analysis and processing of images and videos, data mining and intelligent processing applications of various information. Since the course teaching focuses on the description of algorithms, students will not find it boring and can master intelligent ideas well through practical exercises combined with programming.
Total of 40 lessons8 hours and 47 minutes and 20 seconds
This course is a new series of video tutorials on artificial intelligence and deep learning launched by Zhengdian Atom in 2023.
Total of 77 lessons1 days and 9 hours and 59 minutes and 46 seconds
Machine learning algorithm basics
Total of 79 lessons20 hours and 14 minutes and 25 seconds
This course is a preparation for the RV1126 AI development board, which is a step-by-step guide for you to learn about the RV1126 AI development board. The supporting development board for this course is the RV1126 AI development board.
Total of 19 lessons3 hours and 38 minutes and 23 seconds
Particle Swarm Optimization (Particle Swarm Optimization) Episode 22 Shi Yuhui
Total of 22 lessons8 hours and 30 minutes and 43 seconds
Total of 37 lessons1 days and 4 hours and 17 minutes and 5 seconds
Learn opencv in 10 hours and show you the charm of simple image processing
Total of 67 lessons8 hours and 33 minutes and 11 seconds
This course is an undergraduate course in the Department of Intelligent Science and Technology, School of Computer Science, Beijing University of Posts and Telecommunications. The course focuses on the basic tasks of computer vision and focuses on introducing some conventional processing ideas and classic processing methods of computer vision tasks.
Total of 18 lessons21 hours and 29 minutes and 20 seconds
Computer Vision and Deep Learning (Graduate Course) Lu Peng, Beijing University of Posts and Telecommunications
Total of 16 lessons23 hours and 24 minutes and 48 seconds
The video mainly explains the commonly used algorithm acceleration methods in the implementation process of image algorithm development. This tutorial uses ARM embedded as the research basis and Raspberry Pi 4 as the experimental platform. The main content includes: 1: List some feasible embedded applications. Image acceleration method; 2: Detailed explanation one by one; 3: Computer demonstration of the code one by one.
Total of 1 lessons35 minutes and 1 seconds
Total of 52 lessons20 hours and 23 minutes and 45 seconds
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