527 skills found · Page 5 of 18
skprasad117 / Predicting Student Performance Using Machine LearningUsing machine learning algorithms, this project aims to predict student performance in standardized tests based on demographic and academic data.
aws-samples / Amazon Sagemaker Ab Testing PipelineAmazon SageMaker MLOps deployment pipeline for A/B Testing of machine learning models.
antonio-f / Adversarial TaskGenerative Adversarial Networks test from Coursera's Advanced Machine Learning. Executed on Colab.
exactpro / Nostradamus🧠 An open-source machine learning application for analyzing software defect reports extracted from bug tracking systems.
LOVE-DOCTOR / MultiTrainTest several machine learning models on your dataset with few lines of code
aws-solutions / Improving Forecast Accuracy With Machine LearningThe Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.
kingabzpro / Automating Machine Learning TestingAutomating Machine Learning Testing using GitHub Actions and DeepChecks
PaacMaan / Cars Price PredictorThis project shows step by step how to build a machine learning model that can predict the price of a car based on its features by testing many Machine Learning models and comparing between them.
data-max-hq / Ab Testing In MlDeploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
AnushaUpadhyaya / Intrusion Detection SystemI have tried some of the machine learning and deep learning algorithm for IDS 2017 dataset. The link for the dataset is here: http://www.unb.ca/cic/datasets/ids-2017.html. By keeping Monday as the training set and rest of the csv files as testing set, I tried one class SVM and deep CNN model to check how it works. Here the Monday dataset contains only normal data and rest of the days contains both normal and attacked data. Also, from the same university (UNB) for the Tor and Non Tor dataset, I tried K-means clustering and Stacked LSTM models in order to check the classification of multiple labels.
mudigosa / Image ClassifierImage Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree
yiguanxian / Disk Failure PredictionIn this case study, you can download SMART logs from Backblaze website(https://www.backblaze.com/b2/hard-drive-test-data.html), then design and implement a machine learning based solution to predict the disk failures in daily(output prediction results in each testing day) granularity.
TommyLemon / Unitauto GoUnitAuto - ☀️ 最先进、最省事、ROI 最高的单元测试，机器学习 零代码、全方位、自动化 测试 方法/函数，用户包含腾讯、快手、某 500 强巨头等，本项目为 Go 版 ☀️ The most advanced unit testing way powered by machine learning. Coding-free, comprehensive and automatic testing for methods/functions. Used by Tencent, Kwai, a Fortune 500 company, etc. This repo is the Go version
popdotco / WinstonWinston is a PHP AB/split testing library utilizing Redis and a machine learning algorithm for automatically displaying the most successful test variations over time.
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They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)
Hazrat-Ali9 / Software Engineering With ML AI🚞 Software 🚂 Engineering 🚃 with the 🚅 Power of 🚋 Machine 🚁 Learning 🛳 AI Adaptive ✈ systems 🛩 smart 🚀 refactoring 🚢 self-healing 🛸Test case 🚟 generation ⏰ bug ☂ prediction ⛱ and auto 🛬 remediation 🚝 Embedding 🕌 AI into 🏚 user 🏭 facing 🎠 systems 🚒
BioinfoMachineLearning / MULTICOM3The software system of improving AlphaFold2- and AlphaFold-Multimer-based protein tertiary & quaternary structure prediction. It was developed by the Bioinformatics and Machine Learning Lab at the University of Missouri. It was blindly tested in CASP15 and ranked among the best server predictors in 2022. It improves AlphaFold's accuracy by 5-10%.
nschaetti / SFGram DatasetSFGram (Science-Fiction Gram) is a dataset of public science-fiction novels, books and movie covers. It is designed to be used by researchers to study the evolution of the science-fiction literature over time and to test machine learning algorithms on authorship attribution and document classification tasks. All the documents are now published on the public domain and were obtained from the Gutenberg project or the archive.org website.
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w3-button" href="/w3css/default.asp">Learn W3.CSS</a> <a class="w3-bar-item w3-button" href="/colors/default.asp">Learn Colors</a> <a class="w3-bar-item w3-button" href="/icons/default.asp">Learn Icons</a> <a class="w3-bar-item w3-button" href="/graphics/default.asp">Learn Graphics</a> <a class="w3-bar-item w3-button" href="/graphics/svg_intro.asp">Learn SVG</a> <a class="w3-bar-item w3-button" href="/graphics/canvas_intro.asp">Learn Canvas</a> <a class="w3-bar-item w3-button" href="/howto/default.asp">Learn How To</a> <a class="w3-bar-item w3-button" href="/sass/default.php">Learn Sass</a> <div class="w3-hide-large w3-hide-small"> <h3 class="w3-margin-top">Data Analytics</h3> <a class="w3-bar-item w3-button" href="/ai/default.asp">Learn AI</a> <a class="w3-bar-item w3-button" href="/python/python_ml_getting_started.asp">Learn Machine Learning</a> <a class="w3-bar-item w3-button" href="/datascience/default.asp">Learn Data Science</a> <a class="w3-bar-item w3-button" href="/python/numpy/default.asp">Learn NumPy</a> <a class="w3-bar-item w3-button" href="/python/pandas/default.asp">Learn Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item 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href="/java/default.asp">Learn Java</a> <a class="w3-bar-item w3-button" href="/c/index.php">Learn C</a> <a class="w3-bar-item w3-button" href="/cpp/default.asp">Learn C++</a> <a class="w3-bar-item w3-button" href="/cs/index.php">Learn C#</a> <a class="w3-bar-item w3-button" href="/r/default.asp">Learn R</a> <a class="w3-bar-item w3-button" href="/kotlin/index.php">Learn Kotlin</a> <a class="w3-bar-item w3-button" href="/go/index.php">Learn Go</a> <a class="w3-bar-item w3-button" href="/django/index.php">Learn Django</a> <a class="w3-bar-item w3-button" href="/typescript/index.php">Learn TypeScript</a> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">Server Side</h3> <a class="w3-bar-item w3-button" href="/sql/default.asp">Learn SQL</a> <a class="w3-bar-item w3-button" href="/mysql/default.asp">Learn MySQL</a> <a class="w3-bar-item w3-button" href="/php/default.asp">Learn PHP</a> <a class="w3-bar-item w3-button" href="/asp/default.asp">Learn ASP</a> <a class="w3-bar-item w3-button" href="/nodejs/default.asp">Learn Node.js</a> <a class="w3-bar-item w3-button" href="/nodejs/nodejs_raspberrypi.asp">Learn Raspberry Pi</a> <a class="w3-bar-item w3-button" href="/git/default.asp">Learn Git</a> <a class="w3-bar-item w3-button" href="/aws/index.php">Learn AWS Cloud</a> <h3 class="w3-margin-top">Web Building</h3> <a class="w3-bar-item w3-button" href="https://www.w3schools.com/spaces" target="_blank" onclick="ga('send', 'event', 'spacesFromTutorialsAcc', 'click');" title="Get Your Own Website With W3schools Spaces">Create a Website <span class="ribbon-topnav ws-yellow">NEW</span></a> <a class="w3-bar-item w3-button" href="/where_to_start.asp">Where To Start</a> <a class="w3-bar-item w3-button" href="/w3css/w3css_templates.asp">Web Templates</a> <a class="w3-bar-item w3-button" href="/browsers/default.asp">Web Statistics</a> <a class="w3-bar-item w3-button" href="/cert/default.asp">Web Certificates</a> <a class="w3-bar-item w3-button" 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Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <a class="w3-bar-item w3-button" href="/googlesheets/index.php">Learn Google Sheets</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item w3-button" href="/xml/xpath_intro.asp">Learn XPath</a> <a class="w3-bar-item w3-button" href="/xml/xquery_intro.asp">Learn XQuery</a> </div> </div> </div> <br class="hidesm"> </nav> <nav id="nav_references" class="w3-hide-small" style="position: absolute; padding-bottom: 60px; display: none;"> <div class="w3-content" style="max-width:1100px;font-size:18px"> <span onclick="w3_close_nav('references')" class="w3-button w3-xxxlarge w3-display-topright w3-hover-white sectionxsclosenavspan" style="padding-right:30px;padding-left:30px;">×</span><br> <div class="w3-row-padding w3-bar-block"> <div class="w3-container" style="padding-left:13px"> <h2 style="color:#FFF4A3;"><b>References</b></h2> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">HTML</h3> <a class="w3-bar-item w3-button" href="/tags/default.asp">HTML Tag Reference</a> <a class="w3-bar-item w3-button" href="/tags/ref_html_browsersupport.asp">HTML Browser Support</a> <a class="w3-bar-item w3-button" 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href="/bootstrap4/bootstrap_ref_all_classes.asp">Bootstrap 4 Reference</a> <a class="w3-bar-item w3-button" href="/w3css/w3css_references.asp">W3.CSS Reference</a> <a class="w3-bar-item w3-button" href="/icons/icons_reference.asp">Icon Reference</a> <a class="w3-bar-item w3-button" href="/sass/sass_functions_string.php">Sass Reference</a> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">JavaScript</h3> <a class="w3-bar-item w3-button" href="/jsref/default.asp">JavaScript Reference</a> <a class="w3-bar-item w3-button" href="/jsref/default.asp">HTML DOM Reference</a> <a class="w3-bar-item w3-button" href="/jquery/jquery_ref_overview.asp">jQuery Reference</a> <a class="w3-bar-item w3-button" href="/angular/angular_ref_directives.asp">AngularJS Reference</a> <a class="w3-bar-item w3-button" href="/appml/appml_reference.asp">AppML Reference</a> <a class="w3-bar-item w3-button" href="/w3js/w3js_references.asp">W3.JS Reference</a> <h3 class="w3-margin-top">Programming</h3> <a class="w3-bar-item w3-button" href="/python/python_reference.asp">Python Reference</a> <a class="w3-bar-item w3-button" href="/java/java_ref_keywords.asp">Java Reference</a> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">Server Side</h3> <a class="w3-bar-item w3-button" href="/sql/sql_ref_keywords.asp">SQL Reference</a> <a class="w3-bar-item w3-button" href="/mysql/mysql_ref_functions.asp">MySQL Reference</a> <a class="w3-bar-item w3-button" href="/php/php_ref_overview.asp">PHP Reference</a> <a class="w3-bar-item w3-button" href="/asp/asp_ref_response.asp">ASP Reference</a> <h3 class="w3-margin-top">XML</h3> <a class="w3-bar-item w3-button" href="/xml/dom_nodetype.asp">XML DOM Reference</a> <a class="w3-bar-item w3-button" href="/xml/dom_http.asp">XML Http Reference</a> <a class="w3-bar-item w3-button" href="/xml/xsl_elementref.asp">XSLT Reference</a> <a class="w3-bar-item w3-button" href="/xml/schema_elements_ref.asp">XML Schema Reference</a> </div> <div class="w3-col l3 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Previous</span></a> <a class="w3-btn" href="default.asp"><span class="w3-hide-small">Complete HTML </span>Reference</a> <a class="w3-right w3-btn" href="tag_param.asp"><span class="w3-hide-small">Next </span>❯</a> </div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan"><p></code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan"><p></code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;"><p></td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>html<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>style<span class="tagcolor" style="color:mediumblue">></span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/style<span class="tagcolor" style="color:mediumblue">></span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>body<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/body<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/html<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p> More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan"><p></code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> </span></div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_default_css" class="w3-btn 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coneypo / Smile DetectorTrain and test Smile-Detector Machine Learning models / 利用机器学习模型训练和检测笑脸
