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Click to follow the "Technology Research Bar" above, select to add the "Star Logo" and place it at the top of the heavyweight dry goods listoneTime Delivery Latest Full Process ChatGPT OfficetakeAdvanced applications in scientific research applications, paper writing, data analysis, machine learning, deep learning, and AI drawingone4-day ChatGPT course taught

On November three0, two022, it may becomeoneA day that changed human history - OpenAI, an American artificial intelligence development agency, has launched the chatbot ChatGPT three.5, pushing the development of artificial intelligence forwardoneIn April 2023, a stronger version of ChatGPT 4.0 will be launched, with multimodal interaction methods such as text, voice, and images, making it more widely used in various industriesThe possibility.

On November seven, 2023, OpenAI's first developer conference, known as the "Spring Festival Gala of the tech industry," attracted the attention of users around the world. The GPT store also demonstrated OpenAI's ambition to build an AI ecosystem. Therefore, in order to help researchers more proficiently master the powerful features of ChatGPT 4.0 in data analysis, automatic code generation, and systematically learn the basic theoretical knowledge of artificial intelligence (including traditional machine learning, deep learning, etc.), as well as specific code implementation methodsholdChatGPT Deep Research Applications, Data Analysis and Machine Learning, AI MappingtakeThe "Efficient Paper Writing Training Course" aims to help students master various usage methods of ChatGPT 4.0 in scientific research worktakeTechniques, as well as classic machine learning algorithms in the field of artificial intelligence (BP neural network, support vector machine, decision tree, random forest)Forest and variable dimensionality reductiontakeFeature selection, swarm optimization algorithms, and popular deep learning methods such as convolutional neural networks, transfer learning, and RNNtakeThe basic principles of LSTM neural network, YOLO object detection, autoencoder, etc. and the implementation methods of Python and PyTorch code.

This training adopts a combination of "theoretical explanation+practical case study+hands-on operation+discussion and interaction" to thoroughly explain the latest features of ChatGPT 4.0, as well as the experience and skills that classical artificial intelligence methods need to master in practical applicationoneTraining time and method: On site time:

July 24, 2024⑶On site location on the onest: Lhasa Live broadcast time: July 26, 2024⑵Tencent Meeting on August 9th, 2024⑴On site location on August 8th: Hohhot Live broadcast time: August one5th, 2024⑴On site time of Tencent Meeting on the 8th:

2August 28th, 2024⑼On site location: Xi'an Live broadcast time: August 29, 2024⑼Tencent Meeting 2, Meeting Benefits one: Unlimited Learning: Course Participation1Next time, [this course] post conference (free participation in online live streaming)take1Next, on-siteNo,Limited number of times, only for the person attending the conference)

2: Each person can receive one independent ChatGPT account that can be used for free. 3: One month of ChatGPT Plus/4.0 membership function application is given as a gift. 4: All course replays are provided, student groups are established, and long-term communication assistance is provided3The keynote speaker is from a key research university in China, and the associate professor has been engaged in artificial intelligence (AI) for a long timetakeIn the field of big data analysis and related research, we are committed to integrating artificial intelligence technologytakeThe integration of cutting-edge applications in related fields promotes the development of interdisciplinary research, and the team has accumulated a long-term application of AI language models in scientific research workSorryRich practical experience.

4、 Conference expenses are notMembership fee: 3980 yuan. AiShang training membership fee: Membership policy. Attendees can obtain the "AIGC Skill Enhancement" professional skills training certificate and proof of study hours. This certificate can be found online and can be used asMy little selfAn important reference for learning and knowledge updating, professional skill development of in-service personnel in the unit, and talent recruitment in the unit.

Certificate inquiry website: www.aishangyanxiu.com 6. Course outline Course arrangement Learning content1Chapter 2024 Introduction to the Latest Developments in Large Language Models 1. Introduction to the Latest Developments in AIGC Technology in 2024 (Basic Concepts of Generative Artificial Intelligence)takePrinciple, Wensheng Video Model

OpenAI Sora vs.Google Veo）2、 (Practical Exercise) Major Language Models at Home and Abroad (ChatGPT 4O Gemini、Claude、Llama3、 Wenxin1Comparative Analysis of Words, Starfire, Tongyi Qianwen, Kimi, Zhipu Qingyan, etc

3. (Practical Exercise) Local Deployment and Dialogue of Llama3 Open Source Large Language ModeltakeFine tuning training local data 4, (practical exercise) ChatGPT conversation initial experience (registration)takeRecharge and purchase methods) 5. (Practical practice) ChatGPT Research Essential GPT Summary Introduction (Find useful GPT)

GPT model, prompt word optimization, mind map generation, PPT generation, video generation, and developmentnatureDevelop a study plan, search for papers, summarize the content of papers, summarize video content, write papers, translate papers, and polish paperstakeModification, reference format management, paper review, data analysis, code generation, code debugging, etc

6. (Practical Exercise) Introduction to GPT StoretakeUse 7. (Practical Exercise) Customize your own exclusive GPTs (two ways to create exclusive GPTs: chat/configure parametersUse Knowledge to upload local knowledge base to improve dedicated GPT performance, and utilize Actions

Obtaining external information and sharing exclusive GPTs through API) 8. (Practical exercise) ChatGPT conversation record storagetakeManagement Chapter 2 ChatGPT Prompt Word Usage MethodtakeTip 1: (Practical Exercise) ChatGPT Prompt Usage Tips

ChatGPT sets identity, clarifies task content, provides background information related to the task, and provides examples12. (Practical exercise) Common ChatGPT prompt word templates 3. (Practical exercise) ChatGPT prompt word optimization (Promptest)

PromptPerfect, PromptPal Tip Treasure, etc.) 4. (Practical Exercise) ChatGPT breaks through token restrictions to achieve integrationReceive or output 10000 word long text (number of tokens)takeConversion between character counts, five methods for submitting text exceeding token limits, and four methods for making

5. (Practical exercise) Control the output length of ChatGPT (using modifiers, limiting the range of answers, and using...)height6. (Practical exercise) Save your favorite ChatGPT prompt words and1Key call3chapter

ChatGPT 4 helps with daily life and learningtakeJob 1: (Practical Exercise) ChatGPT helps primary and secondary school students with homework guidance (writing essays, grading essays, solving math problems, practicing English listening, speaking, reading, writing, physics calculations, chemical calculations, etc.) 2. (Practical Exercise) ChatGPT helps with copywritingtakeRefine and modify

3. (Practical Exercise) ChatGPT Helps with Family Health Management (Interpretation of Laboratory Test Results, Medical Consultation)takePreliminary diagnosis, common chronic disease management, daily nutrition and dietary advice, etc.) 4. (Practical exercise) ChatGPT helps college students find jobstakeEmployment (resume writing, mock interviews, career planning, etc.)

5. (Practical Exercise) ChatGPT Helps Business Work (Industry Competitor Search)takeAnalysis and Product Creative DesigntakeSuggestions and promotion of marketing strategiestakePlan formulation and contract writing) 6. (Practical exercise) Create exquisite mind maps using ChatGPT 4 7. (Practical exercise) Generate flowcharts and Gantt charts using ChatGPT 4

8. (Practical Exercise) Using ChatGPT to create PPT9, (Practical Exercise) Using ChatGPT to automatically create videos, (Practical Exercise) ChatGPT to assist teachers in efficient lesson preparation (Socratic teaching, for...)No,Generated by students in the same majorNo,Same teaching content) 11

(Practical Exercise) ChatGPT Assistance ProgramEfficient learning (generated using plugins)nature12. Case Demonstration in the Chemical Learning PlantakeChapter 4: ChatGPT Assisted Project Application, Paper Topic Selection, and Experimental Plan Design 1. Skills and Key Points Analysis for Writing a Project Application (Project Name, Keywords, Abstract, Project Basis, References, Research Objectives, Research Content, Research Plan, Key Scientific Issues, Feasibility Analysis, Innovation Points)takecharacteristicThe placeExpected research results, work foundation, etc

2. (Practical Exercise) Use ChatGPT to analyze popular research directions in a designated field. 3. (Practical Exercise) Use ChatGPT to assist in writing and polishing various parts of the project proposal. 4. (Practical Exercise) Use ChatGPT to summarize the limitations of the designated papertakeNo,Feet and provide potential improvement ideastakeproposal

5. (Practical Exercise) Using ChatGPT 4 to Evaluate the Novelty of Specific Improvement IdeastakePublishedSimilar to job 6, (practical exercise) using ChatGPT to enter1Refine the improvement ideas step by step and condense the topic selection of the papertakeInnovation point 7: (Practical exercise) Use ChatGPT 4 to provide specific algorithm steps and automatically generate the algorithm's

Python Example Code Framework 8, (Practical Exercise) Design a complete experimental plan using ChatGPT 4takeData analysis process 9. (Practical exercise) Use ChatGPT 4 to provide the starting point and ideas for the Discussion section of the paper 10. Case demonstrationtakePractical Exercise Chapter 5

ChatGPT helps with information retrieval, summary analysis, and paper writingtakeSubmission, patent idea conceptiontakeWriting of Disclosure Letter 1. (Practical Exercise) Traditional Information Retrieval MethodstakeSummary of Techniques (Google Scholar ResearchGate、Sci-Hub、

GitHub、 Keyword search+peer search2. (Practical Exercise) Using ChatGPT to Implement Online Literature Retrieval 3. (Practical Exercise) Using ChatGPT to ReadtakeSummarize and analyze the content of academic papers (paper)secondaryWork, innovation points, limitationstakeNo,Foot, multi document comparative analysis, etc

4. (Practical Exercise) Using ChatGPT to Interpret the Working Principle of System Block Diagram in the Paper 5. (Practical Exercise) Using ChatGPT to Interpret the Meaning of Mathematical Formulas in the Paper 6. (Practical Exercise) Using ChatGPT to Interpret the Meaning and Conclusion of Data in Charts and Tables in the Paper 7. (Practical Exercise)

Using ChatGPT to Summarize Youtube Video Content 8, (Practical Practice) Using ChatGPT to Complete the Topic Design of Academic PaperstakeOptimization 9: (Practical Exercise) Use ChatGPT to automatically generate the overall framework, abstract, introduction, literature review, and complete length of the paperA paper, etc

10. (Practical Exercise) Use ChatGPT to complete paper translation (specify translation roles and fields, provide background prompts) 11. (Practical Exercise) Use ChatGPT to correct paper grammar 12. (Practical Exercise) Use ChatGPT to polish paragraph structure and sentence logic

13. (Practical Exercise) Using ChatGPT to Reduce Paper Weight 14. (Practical Exercise) Using ChatGPT to Automatically Convert Paper Reference Format 15. (Practical Exercise) ChatGPT to Assist Reviewers in Writing Paper Review Comments 16. (Practical Exercise)

ChatGPT assisted contributors in responding to paper review comments 17. (Practical exercise) ChatGPT literature search, summary of essential GPTs for paper writing 18. (Practical exercise) Utilizing ChatGPT to mine invention patent ideastakeConcept 19: (Practical Exercise)

Using ChatGPT to complete the writing of invention patent disclosure document 20, case demonstrationtakeChapter 6: ChatGPT Helps with Python Programming Introduction, Scientific Computing, Data Visualization, and Data Preprocessing 1. (Practical Exercise) Python Environment Construction (Python, Installation)takeVersion selection;

PyCharm、 Installation; Hello World in Python; The3Installation of square moduletakeuse; Python 2.xtakePython 3. x Comparison) 2. (Practical Exercise) Basic Python syntax (Python variable naming rules; Python)

Basic mathematical operations; Definition of Common Variable Types in PythontakeOperation; Python program comments) 3. (Practical exercise) Python process control (conditional judgment; for loop; whilE-cycle; Break and continue) 4. (Practical exercise) Python

functiontakeDefinition of Object (Function)takeCall; Parameter passing of functionstakeReturn value; Variable ScopetakeGlobal variables; Creation of objectstake5. (Practical Exercise) Installation of MatplotlibtakeGraphic drawing (setting attributes such as scatter points, lines, coordinate axes, legends, annotations, etc.; drawing multiple graphs; nesting graphs; drawing various graphs such as line charts, bar charts, pie charts, maps, etc.)

6. (Practical Exercise) Installation of advanced drawing libraries such as Seaborn, Bokeh, Pyecharts, etctakeUsing (drawing dynamic interaction diagrams, developing big data visualization pages, etc.) 7. (practical exercises) Scientific computing module library (installation of Numpy; ndarray type attributes)takeCreation of arrays; Array indextakesection;

Introduction to Common Numpy Functionstake8. (Practical Practice)Upload local data (Excel/CSV tables, txt text, PDF, images, etc.) using ChatGPT. 9. (Practical exercise) Use ChatGPT to implement image processing (image scaling, rotation, cropping, denoising, etc.)takeGo fuzzy)

10. (Practical Exercise) Using ChatGPT to Implement Descriptive Statistical Analysis (Frequency Analysis of Data: Statistical Histogram; Central Trend Analysis of Data: Correlation Analysis of Data) 11. (Practical Exercise) Common Data Preprocessing Methods (Data Standardization)takereturn1Normalization and data outlierstakeMissing value processing, data discretization and encoding processing, manual generation of new features)

12. (Practical Exercise) Integrating ChatGPT 4takeAutomatic generation of data preprocessing code in PythontakeRun 13, (Practical Exercise) Use ChatGPT to automatically generate data statistical analysis charts 14, (Practical Exercise) Use ChatGPT4 Implementation code line by line explanation

15. (Practical Exercise) Implementing Code Bug Debugging with ChatGPT 4takeAutomatic modification 16. Case demonstrationtakePractical Practice7Chapter ChatGPT 4 Helps Machine Learning Modeling 1. Basic Principles of BP Neural Networks (What are the classifications of artificial neural networks? What is the topology structure and training process of BP neural networks?)? What is gradientwhereaboutsThe law

2. (Practical Exercise) Python Code Implementation of BP Neural Network (Dividing Training Set and Test Set, Data Conversion)13. (Practical Exercise) Optimization of BP Neural Network Parameters (How to set the number of hidden layer neurons, learning rate, initial weights, and thresholds? What is cross validation?) 4. (Practical Exercise)

Several issues worth studying (underfitting)takeOverfitting, selection of evaluation indicators, sample sizeNo,Balance, etc.) 5. (Practical exercise) ChatGPT prompt word library explanation in BP neural network6. (Practical Exercise) Using ChatGPT 4 to Automatically Generate Code for BP Neural Network ModeltakeRun 7

The working principle of SVM (what is the role of kernel function? What is support vector? How to solve multi classification problems?) 8. The working principle of decision tree (what are information entropy and information gain? The difference between ID3 algorithm and C4.5 algorithm)take9. Working principle of random forest（WhyDo you need a random forest algorithm? generalizedtakeWhat does the narrow definition of "random forest" refer to? What is the essence of 'randomness'? How to visualize and interpret the results of random forests

10. BaggingtakeThe difference between boostingtakeContact 11. Working principle of AdaBoost vs. Gradient Boosting 12. Common GBDT algorithm frameworks (XGBoost, LightGBM) for practical exercises

13（Practical Exercise: ChatGPT Hint Lexicon Explanation in Decision Tree, Random Forest, XGBoost, and LightGBM 14. (Practical Exercise) Using ChatGPT 4 to Automatically Generate Code for Decision Tree, Random Forest, XGBoost, and LightGBM Modelstakefunction

15. Case demonstrationtakePractical Exercise Chapter 8 ChatGPT 4 Helps Machine Learning Model Optimization: Variable Dimensionality ReductiontakeFeature selection 1. Basic principles of principal component analysis (PCA) 2. Basic principles of partial least squares (PLS) 3. Common feature selection methods (optimization search

Filter and Wrar, etc; Forward directiontakeBackward selection method; Interval method; Non informative variable elimination method; 4. Basic principles of Genetic Algorithm (GA) (represented by genetic algorithm for swarm optimization)What is the basic idea of transformation algorithm? Selection, crossover, mutation3What are the functions of each operator

5. (Practical Exercise) ChatGPT prompt word library explanation for PCA, PLS, feature selection, and group optimization algorithms 6. (Practical Exercise) Implementing variable dimensionality reduction using ChatGPT 4 and pluginstakeAutomatic generation of code for feature selection algorithmtakeRun 7, Case DemonstrationtakePractical Exercise Chapter 9

ChatGPT 4 Helps Convolutional Neural Network Modeling 1. Introduction to Deep Learning (Memoirs of Deep Learning, Deep Learning)takeThe difference between traditional machine learningtake2. Basic principles of convolutional neural networks (what are convolutional kernels and pooling kernels? What is the typical topology structure of CNN?)? What is the weight sharing mechanism of CNN

3. The evolutionary history of convolutional neural networks: LeNet, AlexNet, Vgg⑴6/19, GoogLeNet, ResNet, etcThe difference between classical deep neural networkstakeContact 4: (Practical Exercise) Using PyTorch to Build Convolutional Neural Networks (Convolution)

Layer, Batch Normalization Layer, Pooling Layer, Dropout Layer, Flatten Layer, etc.) 5. (Practical Exercise) Parameter tuning techniques for convolutional neural networks (convolution kernel size, number of convolution kernels, movement step size, zero padding operation, pooling kernel size, etc.)takeWhat is the relationship between the dimensions of feature maps and the number of model parameters

6. (Practical Exercise) ChatGPT Hint Lexicon Explanation in Convolutional Neural Networks 7. (Practical Exercise) Using ChatGPT 4 to Automatically Generate Code for Convolutional Neural Network ModelstakeRun (1) CNN pre trained model to achieve object recognition; (2) Extracting abstract features using convolutional neural networks;

(3) Custom Convolutional Neural Network Topology 8, Case StudyshowtakePractical Exercise Chapter 10 ChatGPT 4 Helps with Transfer Learning Modeling 1. Basic Principles of Transfer Learning Algorithms 2. (Practical Exercise) Transfer Learning Algorithms Based on Deep Neural Network Models 3. (Practical Exercise) ChatGPT in Transfer Learning

Tip Lexicon Explanation 4: (Practical Exercise) Using ChatGPT to Automatically Generate Code for Transfer Learning ModelstakeRun 5, Practical Practice Tenth1Chapter ChatGPT 4 Helps RNN and LSTM Modeling 1. Basic Working Principle of Recurrent Neural Network RNN 2. Basic Working Principle of Long Short Term Memory Network LSTM

3. (Practical Exercise) RNNtakeChatGPT prompt word library explanation in LSTM 4. (Practical exercise) Using ChatGPT 4 and plugins to automatically generate code for RNN and LSTM modelstakeRun 5, Case DemonstrationtakePractical Exercise Chapter 12 ChatGPT 4 Helps YOLO Object detection modeling

1. What is object detection? object detection takeThe difference in target recognitiontakeContact 2: Working principle of YOLO model, YOLO modeltakeDifferences between traditional object detection algorithms 3. (Practical exercise) Explanation of ChatGPT prompt lexicon in YOLO model 4. (Practical exercise) Using ChatGPT 4 and plugins to automatically generate code for YOLO object detection modeltakefunction

(1) Using pre trained YOLO models to achieve object detection (image detection, video detection, real-time camera detection); (2) Data annotation demonstration (Introduction to LabelImage usage); (3) Train your own object detection dataset 5. Case demonstrationtakePractical Exercise No.103

章ChatGPT4助力机器学习取深度学习建模的行业应用1、（实操演练）利用ChatGPT4实现近红外光谱分析模型的建立、代码自动生成取运行2、（实操演练）利用ChatGPT4实现生物医学信号（时间序列、图像、视频数据）分类识别取回归拟合模型的建立、代码自动生成取运行

3、（实操演练）利用ChatGPT4实现遥感图像目标检测、地物分类及语义分割模型的建立、代码自动生成取运行4、（实操演练）利用ChatGPT4实现大气污染物预测模型的建立、代码自动生成取运行5、（实操演练）

利用ChatGPT4实现自然语言处理模型的建立、代码自动生成取运行6、案例演示取实操练习 第十四章ChatGPT 4助力AI绘图技术1、（实操演练）利用ChatGPT4 DALL.E 3生成图像（图像、修改图像）

2、（实操演练）ChatGPT4 DALL.E 3常用的提示词库（广告海报、Logo、3D模型、插画、产品包装、烹饪演示、产品外观设计、UI设计、吉祥物设计等）3、（实操演练）ChatGPT4 DALL.E 3

中的多种视图（正视图、后视图、侧视图、四分之3视图、鸟瞰视图、全景视图、第1人称视角、分割视图、截面视图等）4、（实操演练）ChatGPT4 DALL.E 3中的多种光效（电致发光、化学发光、生物荧光、极光闪耀、全息光等）

5、（实操演练）ChatGPT4 DALL.E 3格子布局取角色1致性的实现6、（实操演练）ChatGPT4 DALL.E 3生成动图GIF7、（实操演练）Midjourney工具使用讲解8、（实操演练）

Stable Diffusion工具使用讲解9、（实操演练）Runway图片生成动画工具使用讲解10、案例演示取实操练习第十五章GPT 4 API接口调用取完整项目开发1、（实操演练）GPT模型API接口的调用方法（

API Key的申请、API Key接口调用方法取参数说明）2、（实操演练）利用GPT4实现完整项目开发（1）聊天机器人的开发（2）利用GPT API和Text Embedding生成文本的特征向量（3

）构建基于多模态（语音、文本、图像）的阿尔茨海默病早期筛查程序3、案例演示取实操练习 第十六章面向科研场景的ChatGPT提示词工程大赛【科研创意Prompt挑战】活动背景：为了提升科研人员在科研过程中的提示词撰写能力，特举行

ChatGPT培训课程，并在课程中加入【提示词大赛】环节，通过比赛形式激发学员的创意和实践能力活动目标：通过【提示词大赛】，提高学员在科研过程中撰写提示词的能力，激发创意取实践结合，为未来的科研工作提供更好的支持和帮助。

参赛对象：参加本次ChatGPT培训课程的所有科研人员赛题内容：培训课程第1天结束后公布具体赛题，赛题将围绕科研过程中没有同环节的提示词撰写提交方式：学员需在培训课程第3天晚前提交答案，具体提交方式将在赛题公布时1并说明。

奖项设置：1等奖1名、二等奖2名、3等奖3名【设置奖项详细见流程说明】评委评选：由培训导师及特邀评委组成评审团，对所有提交的提示词进行评选评选标准：提示词的创意、准确性、实用性及取科研主题的契合度备注：详细在会议中具体说明。

7、联系方式详细报名流程，请联系课程负责人详细报名流程，请咨询课程负责人贾颖：177⑶266-0520（微电）QQ：3346995394

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35教程最新CMIP6数据处理及在气候变化、水文、生态等领域中的实践技术应36教程基于“python+”潮汐、风驱动循环、风暴潮等海洋水动力模拟实践37教程双碳目标下基于全球模式比较计划CMIP6取区域气候-化学耦合模式WRF-Chem的未来大气污染变化模拟

38教程双碳目标下太阳辐射预报模式【WRF-SOLAR】及改进技术在气象、农林、电力等相关领域中的实践应用39教程大气污染扩散模型Calpuff实践技术应用40教程陆面生态水文模拟取多源遥感数据同化的实践技术应用

41教程遥感、GIS和GPS技术在水文、气象、灾害、生态、环境及卫生等领域中的实践应用42教程气象数据相关分析及使用系列课程1：基于CALMET诊断模型的高时空分辨率精细化风场模拟技术应用43教程气象数据相关分析及使用系列课程二：如何使用格点数据分析中国积温变化技术应用

44教程气象数据相关分析及使用系列课程3：如何使用格点数据分析中国霜冻灾害变化技术应用45教程气象数据相关分析及使用系列课程四：遥感降水评估技术方法46教程Python支持下最新Noah-MP陆面模式站点、区域模拟及可视化分析技术应用

47教程高精度气象模拟WRF(Weather ResearchForecasting)实践技术及案例应用48教程基于Python机器学习、深度学习技术提升气象、海洋、水文领域实践应用49教程基于R语言机器学习方法在生态经济学领域中的实践技术应用

50教程基于AERMOD模型在大气环境影响评价中的实践技术应用教程+课件资料及数据代码+导师随行辅导联系课程专员，最高享受7.5折优惠人工智能语言类精品教程推荐（点击标题查看详细内容）1教程R语言结构方程模型（SEM）在生态学领域中的实践应用

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教程基于R语言的极值统计学及其在相关领域中的应用 7教程R语言回归及混合效应（多水平/层次/嵌套）模型应用及贝叶斯 8教程最新基于Python科研数据可视化实践技术应用 9教程全套Python机器学习核心技术取案例分析实践应用视频课程

10教程基于pytorch深度学习遥感影像地物分类取目标识别、分割实践技术11教程R语言贝叶斯方法在生态环境领域中的高阶技术应用12教程如何使用python网络爬虫批量获取公共资源数据实践技术应用13教程

MATLAB 机器学习、深度学习实践应用高级课程14教程基于MATLAB机器学习、深度学习在图像处理中的实践技术应用15教程MATLAB近红外光谱分析技术及实践技术应用16教程PyTorch机器学习取深度学习

17教程最新基于MATLAB科研数据可视化实践技术应用18教程生物信息学大数据分析取R语言绘图19教程python语言生物信息多组学大数据深度挖掘取论文整理技巧实20教程基因家族分析及SCI写作技巧实践技术应用

21教程分子动力学LAMMPS模拟实战技术应用22教程R语言空间分析、模拟预测取可视化23教程R语言生物群落（生态）数据统计分析取绘图实践技术应用24教程基于R语言、MaxEnt模型融合技术的物种分布模拟、参数优化方法、结果分析制图取论文写作

25教程Python 数据挖掘取机器学习实践技术应用26教程基于Python近红外光谱分析取机器学、深度学习方法融合技术应用27教程基于R语言地理加权回归、主成份分析、判别分析等空间异质性数据28教程基于GeoDa取R语言的空间数据回归实践技术应用

29教程基于R语言的DICE模型应用30教程R-GIS: 如何用R语言实现GIS地理空间分析及模型预测实践技术应用31教程R语言取作物模型（DSSAT模型）高级应用实战技术应用32教程Matlab高光谱遥感、数据处理取混合像元分解

33教程Python语言在地球科学领域中的应用34教程GPT模型支持下的Python-GEE遥感云大数据分析、管理取可视化技术及多领域案例实践应用35教程基于R语言BIOMOD2模型的物种分布模拟36教程

基于MATLAB的无人机遥感数据预处理取农林植被性状估算37教程基于R语言的现代线性回归实践技术方法38教程基于R语言的分位数回归高级应用39教程R语言在生态环境领域中的应用40教程R语言数据统计分析取ggplot2高级绘图实践应用

41教程基于R语言APSIM模型高级应用及批量模拟42教程基于R语言的物种气候生态位动态量化取分布特征模拟实践技术高级43教程基于R语言piecewiseSEM结构方程模型在生态环境领域实践技术应用44

教程面向课题组团队及科研人员AI培养计划：AI人工智能实践技术系统性教学方案45教程基于PyTorch深度学习技术及实践应用46教程基于pytorch深度学习无人机遥感影像，目标检测，地物分类及语义分割实践技术应用

47教程基于MATLAB长时间序列遥感数据植被物候提取取分析实践应用48教程基于MATLAB长时间序列遥感数据分析（以MODIS数据处理为例）实践操作视频50教程MATLAB的长时间序列遥感数据产品分析暨MODIS NDVILAI多年产品数据批处理分析实践应用

51教程基于ENVI遥感解译的区域生态环境评价案例分析52教程MATLAB在生态环境数据处理取分析中的应用53教程基于python长时间序列遥感数据植被物候提取取分析实践技术应用54教程基于Python长时间序列遥感数据处理及在全球变化、物候提取、植被变绿取固碳分析、生物量估算取趋势分析等领域中的应用

55教程python高光谱遥感数据处理取机器学习实践技术应用56教程Python 数据挖掘取机器学习高级应用57教程基于python常见地球科学数据（ERA5,雪深，积雪覆盖，海温，植被指数，土地利用）处理实践技术应用

58教程基于Python语言快速批量运行DSSAT模型及交叉融合、扩展应用59教程最新基于MATLAB 2023a的机器学习、深度学习实践应用60教程基于MATLAB长时间序列遥感数据处理及在全球变化、物候提取、植被变绿取固碳分析、生物量估算取趋势分析

61教程“Python+”集成技术高光谱遥感数据处理取机器学习深度应用62教程基于”Python+”多技术融合在蒸散发取植被总初级生产力估算中的实践应用63教程最新Python深度学习技术进阶取应用64

教程基于python多光谱遥感数据处理、图像分类、定量评估及机器学习65教程基于ChatGPT4+Python近红外光谱数据分析及机器学习取深度学习建模进阶应用66教程ChatGPT4+Python数据分析取可视化、人工智能建模及论文高效撰写

教程+课件资料及数据代码+导师随行辅导联系课程专员，最高享受7.5折优惠水文，水环境类精品教程推荐（点击标题查看详细内容）1教程“R 语言+遥感”的水环境综合评价方法实战应用视频课程 2教程HEC-RAS1维、二维建模方法及实践技术应用精品视频课程

3教程基于多案例系统学习防洪评价报告编制方法取水流数学模型建模4教程SWMM复杂城市排水系统模型及排水防涝、海绵城市设计等工程实践应用视频课程5教程SWMM排水管网水力、水质建模及在海绵取水环境中的应用

6教程遥感云大数据在灾害、水体取湿地领域典型案例实践及GPT模型应用7教程基于ArcGIS水文分析、HEC-RAS模拟技术在洪水危险性及风险评估8教程ArcGIS在洪水灾害普查、风险评估及淹没制图中的实践技术

9教程GMS地下水数值模拟技术及在地下水环评中的应用10教程基于DEM的水文分析专题课程11教程Delft3D建模、水动力模拟方法及在地表水环境影响评价中的应12教程基于遥感取GIS在滑坡、泥石流风险普查中的实践技术应用

13教程Delft3D水动力－富营养化模型实践技术高级应用精品课程14教程地下水数值模拟Visual modflow Flex实践技术应用精品14教程ArcGIS在水土流失模拟及分析中的实践技术应用培训班

15教程新《生产建设项目水土保持方案技术审查要点》要求下水土保持方案编高级实践技术应用视频课程16教程地下水环评（1级）实践技术及Modflow地下水数值模拟视频17教程AQUATOX水环境取水生态模型实践技术应用视频课程

18教程水土保持方案编制实操取典型案例分析技术应用19教程全流程地下水环境影响评价方法及关键技术取化工、工业园区、固废堆积、矿山等实际项目应用20教程PHREEQC建模及典型案例解析取高阶拓展应用21教程

基于GIS流域水文分析及水库库容计算22教程3维地质建模数据处理高级实践技术应用23教程SWAT模型在水文水资源、面源污染模拟中的实践技术应用及典型案例分析24教程基于FVCOM模型的3维水动力、水交换、溢油物质扩散及输运数值模拟

25教程FVCOM流域、海洋水环境数值模拟方法及实践技术应用26教程岩土工程渗流问题之有限单元法：理论、模块化编程实现、开源程序手把手实操应用27教程基于Delft3D模型的标量输运、波浪、拉格朗日粒子及溢油模型

28教程最新水文水动力模型在城市内涝、城市排水、海绵城市规划设计中深度应用29教程HYPE分布式水文模型建模方法取案例分析实践技术应用精品课30教程合成孔径雷达干涉测量InSAR数据处理、地形3维重建、形变信息提取、监测等实践技术应用

31教程基于GMTSAR合成孔径雷达干涉测量InSAR数据处理、形变信息提取取分析32教程地面沉降数值模拟实践取应用33教程基于Python实现的深度学习技术在水文水质领域应用精品课程34教程HEC-HMS水文模型

35教程AHP层次分析法在水利中的实践技术应用36教程地理信息系统（ArcGIS）在水文水资源、水环境中的实践技术应用及案例分析37教程建设项目水资源论证方法及报告编制取实际案例分析38教程SWAT模型（建模方法、实例应用、高级进阶）

39教程基于站点、模式、遥感多源降水数据融合技术应用40教程山洪径流过程模拟及洪水危险性评价41教程全流程TOUGH系列实践技术应用42教程GIS在地质灾害危险性评估取灾后重建中的实践技术应用及python机器学习灾害易发性评价模型建立取优化进阶应用

43教程HSPF(Hydrological Simulation Program Fortran)模型应用44教程水土保持方案编制：点型项目、市政工程、线型工程、矿山工程、水利工程、取土场/弃渣场、补报项目、水土保持监测验收

45教程基于Surfer取Voxler数据处理及可视化46教程SWMM模型深度技术取案例精讲高级实践应用47教程SWAT模型十7项案例分析实践技术应用48教程R+VIC模型融合实践技术应用及未来气候变化模型预测

49教程基于SWAT-MODFLOW地表水取地下水耦合实践技术应用50教程基于R语言的水文、水环境模型优化技术及快速率定方法取多模型案例实践高级应用51教程最新全流程GMS地下水数值模拟及溶质（包含反应性溶质）运移模拟技术深度应用

52教程Delft3D水动力取泥沙运动模拟实践技术应用教程+课件资料及数据代码+导师随行辅导联系课程专员，最高享受7.5折优惠生态、遥感精品教程推荐（点击标题查看详细内容）1教程土地利用/土地覆盖遥感解译取基于CLUE模型未来变化情景预测技术

2教程MAXENT模型生物多样性生境模拟取保护优先区甄选、保护区布局优化评估实践技术应用及论文写作3教程InVEST模型高级进阶及在生态系统服务供需、碳中和等领域中的应用及论文写作技能提升精品课程4教程

基于GIS探究环境和生态因子对水体、土壤、大气污染物的影响5教程双碳目标下基于“遥感+”融合技术在碳储量、碳收支、碳循环等多领域监测取模拟6教程利用ArcGIS完成基于土地利用量化人类活动的分析及模型构建

7教程结构方程模型【SEM】高阶系列课程暨：非线性、非正态、交互作用及分类变量分析 8教程ArcGIS应用实战专题课程9教程GIS数据制备，空间分析取高级建模实践技术应用课程10教程无人机生态环境监测、图像处理取GIS数据分析综合技术

应用视频11教程“卫星-无人机-地面”遥感数据快速使用及地物含量计算的实现方法实践技术应用12教程统计方法在变量变化及变量间关系分析中的应用13教程【高分论文密码】大尺度空间模拟预测取数字制图14教程PROSAIL模型前向模拟取植被参数遥感提取代码实现课程

15教程北斗/GNSS高精度数据处理暨GAMIT/GLOBK v10.75实践技术应用16教程长时间序列遥感数据分析取代码实现技术应用17教程CASA（Carnegie-Ames-Stanford Approach）模型原理及实践

18教程无人机遥感在农林信息提取中的实现方法取GIS融合应用高级课程19教程无人机遥感图像拼接及处理实践技术专题课程20教程生态系统碳循环模型CENTURY 建模方法取实例应用21教程高光谱遥感数值建模技术及在植被、水体、土壤信息提取领

22教程Biome-BGC生态系统模型取Python融合技术应用23教程Meta分析在生态环境领域中的实践技术应用精品视频课程24教程近地面无人机植被定量遥感取生理参数反演实践技术应用25教程结构方程模型【SEM】高阶系列课程暨：结构方程模型（SEM）时间重复测量数据分析

26教程结构方程模型【SEM】高阶系列课程暨：系统发育数据纳入结构方程模型技术应用27教程结构方程模型【SEM】高阶系列课程暨：非递归（non-recursive）结构方程模型实践技术应用28教程结构方程模型【SEM】高阶系列课程暨：结构方程模型预测问题-直接预测实现途径

29教程结构方程模型【SEM】高阶系列课程暨：嵌套分层数据及数据分组分析30教程结构方程模型【SEM】高阶系列课程暨：非线性、非正态、交互作用及分类变量分析31教程GIS、GPS、RS综合案例实践技术应用

32教程环境土壤物理HYDRUS2D/3D模型实践应用33教程面向高校的基于算法的发明专利申请写作方法34教程系统学习环境土壤物理模型HYDRUS1D/2D/3D建模方法取案例应35教程最新导则下：基于遥感解译取GIS技术环境影响评价图件制作

36教程遥感影像信息提取取案例分析37教程基于”PLUS模型+“生态系统服务多情景模拟预测38教程CLUE模型构建方法、模型验证及土地利用变化情景预测39教程遥感、GIS及GPS在土壤普查、制图及土壤空间数据分析

40教程WOFOST模型取PCSE模型实践技术应用41教程基于遥感数据取DSSAT作物生长模型同化的作物产量估算方法高级培训班的作物产量估算42教程地表蒸散发遥感产品信息提取验证取融合43教程农田通量计算方法取应用

44教程基于Fragstats的土地利用景观格局分析45教程扎根理论分析NVivo原理取技术应用46教程如何利用有限的数据发表更多的SCI论文？——利用ArcGIS探究环境和生态因子对水体、土壤和大气污染物的影响

47教程环境土壤物理Hydrus1D2D模型实践技术应用48教程基于RWEQ模型的土壤风蚀模数估算及其变化归因分析49教程GEE-PIE遥感大数据处理取典型案例实践技术应用50教程环境多介质逸度模型实践技术取典型案例【代码】应用

51教程双碳目标下基于遥感技术的碳库、碳平衡、温室气体、碳循环等多领域监测取模拟52教程基于“遥感+”蓝碳储量估算、红树林信息提取实践技术应用取科研论文写作53教程最新DSSAT作物模建模实践技术方法54

教程GEE遥感云大数据林业应用典型案例实践及GPT模型应用55教程生态碳汇涡度相关监测取通量数据分析实践技术应用56教程植被参数光学遥感反演方法(Python)及遥感取生态模型数据同化算法57教程双碳目标下农田温室气体排放模拟技术

58教程最新基于Citespace、vosviewer、R语言的文献计量学可视化分析技术及全流程文献可视化SCI论文高效写作方法59教程全流程基于最新导则下的生态环境影响评价技术方法及图件制作取案例60

教程遥感影像目标检测：从CNN（Faster-RCNN）到Transformer（DETR）技术应用61教程基于通用优化GAMS的数学建模和优化分析62教程基STELLA系统动态模拟技术及在农业、生态及环境科学中的应用

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