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MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

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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 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 class="w3-col l3 m6"> <h3 class="w3-margin-top">JavaScript</h3> <a class="w3-bar-item w3-button" href="/js/default.asp">Learn JavaScript</a> <a class="w3-bar-item w3-button" href="/jquery/default.asp">Learn jQuery</a> <a class="w3-bar-item w3-button" href="/react/default.asp">Learn React</a> <a class="w3-bar-item w3-button" href="/angular/default.asp">Learn AngularJS</a> <a class="w3-bar-item w3-button" href="/js/js_json_intro.asp">Learn JSON</a> <a class="w3-bar-item w3-button" href="/js/js_ajax_intro.asp">Learn AJAX</a> <a class="w3-bar-item w3-button" href="/appml/default.asp">Learn AppML</a> <a class="w3-bar-item w3-button" href="/w3js/default.asp">Learn W3.JS</a> <h3 class="w3-margin-top">Programming</h3> <a class="w3-bar-item w3-button" href="/python/default.asp">Learn Python</a> <a class="w3-bar-item w3-button" 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" href="/whatis/default.asp">Web Development</a> <a class="w3-bar-item w3-button" href="/tryit/default.asp">Code Editor</a> <a class="w3-bar-item w3-button" href="/typingspeed/default.asp">Test Your Typing Speed</a> <a class="w3-bar-item w3-button" href="/codegame/index.html" target="_blank">Play a Code Game</a> <a class="w3-bar-item w3-button" href="/cybersecurity/index.php">Cyber Security</a> <a class="w3-bar-item w3-button" href="/accessibility/index.php">Accessibility</a> </div> <div class="w3-col l3 m6 w3-hide-medium"> <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> <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" 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target="_top" href="tag_cite.asp">&lt;cite&gt;</a> <a target="_top" href="tag_code.asp">&lt;code&gt;</a> <a target="_top" href="tag_col.asp">&lt;col&gt;</a> <a target="_top" href="tag_colgroup.asp">&lt;colgroup&gt;</a> <a target="_top" href="tag_data.asp">&lt;data&gt;</a> <a target="_top" href="tag_datalist.asp">&lt;datalist&gt;</a> <a target="_top" href="tag_dd.asp">&lt;dd&gt;</a> <a target="_top" href="tag_del.asp">&lt;del&gt;</a> <a target="_top" href="tag_details.asp">&lt;details&gt;</a> <a target="_top" href="tag_dfn.asp">&lt;dfn&gt;</a> <a target="_top" href="tag_dialog.asp">&lt;dialog&gt;</a> <a target="_top" href="tag_dir.asp">&lt;dir&gt;</a> <a target="_top" href="tag_div.asp">&lt;div&gt;</a> <a target="_top" href="tag_dl.asp">&lt;dl&gt;</a> <a target="_top" href="tag_dt.asp">&lt;dt&gt;</a> <a target="_top" href="tag_em.asp">&lt;em&gt;</a> <a target="_top" href="tag_embed.asp">&lt;embed&gt;</a> <a target="_top" href="tag_fieldset.asp">&lt;fieldset&gt;</a> <a target="_top" href="tag_figcaption.asp">&lt;figcaption&gt;</a> <a target="_top" href="tag_figure.asp">&lt;figure&gt;</a> <a target="_top" href="tag_font.asp">&lt;font&gt;</a> <a target="_top" href="tag_footer.asp">&lt;footer&gt;</a> <a target="_top" href="tag_form.asp">&lt;form&gt;</a> <a target="_top" href="tag_frame.asp">&lt;frame&gt;</a> <a target="_top" href="tag_frameset.asp">&lt;frameset&gt;</a> <a target="_top" href="tag_hn.asp">&lt;h1&gt; - &lt;h6&gt;</a> <a target="_top" href="tag_head.asp">&lt;head&gt;</a> <a target="_top" href="tag_header.asp">&lt;header&gt;</a> <a target="_top" href="tag_hr.asp">&lt;hr&gt;</a> <a target="_top" href="tag_html.asp">&lt;html&gt;</a> <a target="_top" href="tag_i.asp">&lt;i&gt;</a> <a target="_top" href="tag_iframe.asp">&lt;iframe&gt;</a> <a target="_top" href="tag_img.asp">&lt;img&gt;</a> <a target="_top" href="tag_input.asp">&lt;input&gt;</a> <a target="_top" href="tag_ins.asp">&lt;ins&gt;</a> <a target="_top" href="tag_kbd.asp">&lt;kbd&gt;</a> <a target="_top" href="tag_label.asp">&lt;label&gt;</a> <a target="_top" href="tag_legend.asp">&lt;legend&gt;</a> <a target="_top" href="tag_li.asp">&lt;li&gt;</a> <a target="_top" href="tag_link.asp">&lt;link&gt;</a> <a target="_top" href="tag_main.asp">&lt;main&gt;</a> <a target="_top" href="tag_map.asp">&lt;map&gt;</a> <a target="_top" href="tag_mark.asp">&lt;mark&gt;</a> <a target="_top" href="tag_meta.asp">&lt;meta&gt;</a> <a target="_top" href="tag_meter.asp">&lt;meter&gt;</a> <a target="_top" href="tag_nav.asp">&lt;nav&gt;</a> <a target="_top" href="tag_noframes.asp">&lt;noframes&gt;</a> <a target="_top" href="tag_noscript.asp">&lt;noscript&gt;</a> <a target="_top" href="tag_object.asp">&lt;object&gt;</a> <a target="_top" href="tag_ol.asp">&lt;ol&gt;</a> <a target="_top" href="tag_optgroup.asp">&lt;optgroup&gt;</a> <a target="_top" href="tag_option.asp">&lt;option&gt;</a> <a target="_top" href="tag_output.asp">&lt;output&gt;</a> <a target="_top" href="tag_p.asp" class="active">&lt;p&gt;</a> <a target="_top" href="tag_param.asp">&lt;param&gt;</a> <a target="_top" href="tag_picture.asp">&lt;picture&gt;</a> <a target="_top" href="tag_pre.asp">&lt;pre&gt;</a> <a target="_top" href="tag_progress.asp">&lt;progress&gt;</a> <a target="_top" href="tag_q.asp">&lt;q&gt;</a> <a target="_top" href="tag_rp.asp">&lt;rp&gt;</a> <a target="_top" href="tag_rt.asp">&lt;rt&gt;</a> <a target="_top" href="tag_ruby.asp">&lt;ruby&gt;</a> <a target="_top" href="tag_s.asp">&lt;s&gt;</a> <a target="_top" href="tag_samp.asp">&lt;samp&gt;</a> <a target="_top" href="tag_script.asp">&lt;script&gt;</a> <a target="_top" href="tag_section.asp">&lt;section&gt;</a> <a target="_top" href="tag_select.asp">&lt;select&gt;</a> <a target="_top" href="tag_small.asp">&lt;small&gt;</a> <a target="_top" href="tag_source.asp">&lt;source&gt;</a> <a target="_top" href="tag_span.asp">&lt;span&gt;</a> <a target="_top" href="tag_strike.asp">&lt;strike&gt;</a> <a target="_top" href="tag_strong.asp">&lt;strong&gt;</a> <a target="_top" href="tag_style.asp">&lt;style&gt;</a> <a target="_top" href="tag_sub.asp">&lt;sub&gt;</a> <a target="_top" href="tag_summary.asp">&lt;summary&gt;</a> <a target="_top" href="tag_sup.asp">&lt;sup&gt;</a> <a target="_top" href="tag_svg.asp">&lt;svg&gt;</a> <a target="_top" href="tag_table.asp">&lt;table&gt;</a> <a target="_top" href="tag_tbody.asp">&lt;tbody&gt;</a> <a target="_top" href="tag_td.asp">&lt;td&gt;</a> <a target="_top" href="tag_template.asp">&lt;template&gt;</a> <a target="_top" href="tag_textarea.asp">&lt;textarea&gt;</a> <a target="_top" href="tag_tfoot.asp">&lt;tfoot&gt;</a> <a target="_top" href="tag_th.asp">&lt;th&gt;</a> <a target="_top" href="tag_thead.asp">&lt;thead&gt;</a> <a target="_top" href="tag_time.asp">&lt;time&gt;</a> <a target="_top" href="tag_title.asp">&lt;title&gt;</a> <a target="_top" href="tag_tr.asp">&lt;tr&gt;</a> <a target="_top" href="tag_track.asp">&lt;track&gt;</a> <a target="_top" href="tag_tt.asp">&lt;tt&gt;</a> <a target="_top" href="tag_u.asp">&lt;u&gt;</a> <a target="_top" href="tag_ul.asp">&lt;ul&gt;</a> <a target="_top" href="tag_var.asp">&lt;var&gt;</a> <a target="_top" href="tag_video.asp">&lt;video&gt;</a> <a target="_top" href="tag_wbr.asp">&lt;wbr&gt;</a> </div> <br><br> </div> </div> </div> <div class="w3-main w3-light-grey" id="belowtopnav" style="margin-left: 220px; padding-top: 44px;"> <div class="w3-row w3-white"> <div class="w3-col l10 m12" id="main"> <div id="mainLeaderboard" style="overflow:hidden;"> <!-- MainLeaderboard--> <!--<pre>main_leaderboard, all: [728,90][970,90][320,50][468,60]</pre>--> <div id="adngin-main_leaderboard-0" data-google-query-id="CJPA_sueqvcCFXiOSwUd2fYBLg"><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1__container__" style="border: 0pt none;"><iframe id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" name="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" title="3rd party ad content" width="728" height="90" scrolling="no" marginwidth="0" marginheight="0" frameborder="0" role="region" aria-label="Advertisement" tabindex="0" srcdoc="" data-google-container-id="7" style="border: 0px; vertical-align: bottom;" data-load-complete="true"><div style="position: absolute; width: 0px; height: 0px; border: 0px; padding: 0px; margin: 0px; overflow: hidden;"><button></button><a href="https://yahoo.com"></a><input></div></iframe></div></div> <!-- adspace leaderboard --> </div> <h1>HTML <span class="color_h1">&lt;p&gt;</span> Tag</h1> <div class="w3-clear w3-center nextprev"> <a class="w3-left w3-btn" href="tag_output.asp">❮<span class="w3-hide-small"> 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">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;p&gt;</code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan">&lt;p&gt;</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;">&lt;p&gt;</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">&lt;p&gt;</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">&lt;p&gt;</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">&lt;</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">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;</span>html<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; 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>&nbsp; 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">&lt;</span>/style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/html<span class="tagcolor" style="color:mediumblue">&gt;</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>&nbsp;More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</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">&lt;p&gt;</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>&nbsp; display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; 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>&nbsp; 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>&nbsp; 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>&nbsp;&nbsp;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> 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An Enthusiastic undergraduate with a passion for Data Science and Machine learning. With over a year of hands-on experience in the field, I'm constantly exploring the exciting world of AI and innovation. I've built a solid foundation in machine learning and Python.

Python for Data Science, AI & Development - offered by IBM on Coursera

A practical, notebook-based AI and data science handbook for learners and researchers. Built with Python & Jupyter.

The Python labs for the course Data Science & AI (English taught)

Just enough Python for Data Science, Machine Learning, Deep Learning and AI

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them. In this session we will learn how to use Automated Machine Learning to create and apply a binary prediction model in Power BI using Python, Anaconda and Pycaret to quickly classify data.

📘 Official repository for CSI Python Special Interest Group (SIG) 2025. Contains session slides, assignments, example codes, and project materials for Python, Data Science, Machine Learning, AI, Generative AI, and Agentic AI.

A targeted resource for mastering Hugging Face libraries in Python, featuring practice problems, code examples, and interview-focused NLP concepts. Covers model fine-tuning, tokenization, and deployment for excelling in AI and data science interviews.

"Unlock the ultimate beginner's guide to data science! Our curated repository is packed with ML, DL, NLP, and Python essentials, offering clear pathways and valuable insights for your journey. Dive into data analysis and AI effortlessly with best-in-class tools and tutorials tailored for beginners."

🛠️ PyLibPro: A streamlined Python library installer for ML/AI development. Interactive CLI for managing TensorFlow, PyTorch, scikit-learn & more. Install by category or individually. Perfect for data science setup & teaching environments.

This repository is a collection of practical Python coding problems for Data Science, Machine Learning, and AI interview preparation.

For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

Course link: https://cognitiveclass.ai/courses/python-for-data-science/ Python for Data Science these are notebooks from the course This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this free course is for you.

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Cognitiveclass.ai - PY0101EN Python for Data Science

Sri Venkateshwara University (SVU) strives to create professionals who are not only adept in academics but also in application for the benefit of humanity. We foster a culture of learning by doing. We believe in nurturing students who are at the forefront of innovation by offering an environment of research & development to make us Best University in Uttar Pradesh (UP). SVU believes in experiential learning. To facilitate this, we have an ultra-modern infrastructure that motivates students to experiment & excel in their area of interest. The Best University of Moradabad has laboratories & workshops that signify our commitment to core research, thus enabling innovation. SVU is the only institution to have set up labs in collaboration with the industry. This way we can train our students on the latest skills & make them employable. Students sharpen their practical skills under the watch full eyes of trainers & become competent professionals. For the overall development of the students, we organize cultural programs. Students take part in these programs & exhibit their talent to become confident professionals. The annual fest attracts students from all over the country & showcase their talent to make us the Top University in India. We equipped the computing labs with the latest software & hardware to augment the technical skills of the students. SVU’s library is an epitome of knowledge. It has over 3000 books & journals that ensure the students are never short on intellectual input. The team of industry trainers educate them on the key skills so crucial for employment & make us the Best University in Gajraula. The specially created engineering labs assist engineers to refine their technical acumen so much needed for the country. The Chairman Dr. Sudhir Giri believes in removing all the economic & social barriers that can hinder education. Hence, SVU provides many scholarships & grants to meritorious students. Up till now, the college has enabled over 500000 students to attain their academic desires to make us the Best Private University in Uttar Pradesh (UP). The group is running a dozen educational institutions that include medical colleges in India & abroad. Our commitment towards education & healthcare has enabled Dr Sudhir Giri to win the International Glory Man of the year Award 2021. The Best Private University in Moradabad is on the Delhi Moradabad highway, well connected with rail & road. The green surroundings provide peace of mind that enables research based learning. The carefully recruited faculty is the pride of the university. They have years of industrial & academic experience so vital for the students. They transfer key skills & make us the Best Private University in Gajraula. The faculty encourages students to undertake research & sharpen their skills that will enable them to get jobs. Majority of the faculty members are doctorates who educate the students to become competent professionals. The faculty takes part in FDP in order to develop a culture of research. The specialty of SVU is the internship. We have partnered with leading industries for providing internship to the students. We believe that education without applicability is incomplete. Students gain hands on exposure through internship & become job ready. We place most of the students during internship to make us the Top University in India. SVU, the Best University in Uttar Pradesh (UP), adopts a futuristic teaching pedagogy. We strive for experiential learning of our students through role plays, projects & presentation. The students take part in the learning activity & imbibe concepts that enable their placements. The AC seminar & conference halls allow knowledge dispersion for the development of the students. The University is running over 150 undergraduate (UG), postgraduate (PG) courses, (Ph.D.), diploma and certificate courses in various fields of Applied Sciences, Medical Science, Humanities & Social Sciences. We also run courses in Languages, Design, Agriculture, Engineering & Technology, Nursing, Pharmacy, Paramedical, Commerce & Management, Law, Library & information Sciences, Mass Comm. & Journalism to enhance the employability of the youth. SVU has a culture of project based learning. Students do projects in each semester under the guidance of faculty. They complete these projects in earmarked industries to garner hands-on skills. Through these projects, we train students on the hot skills so crucial for employment to make us the Best University in Moradabad. SVU’s Research & Development (R&D) wing encourages students to work on research areas important for the country. We have partnered with leading research institutions to undertake research. The breath-taking infrastructure of the best university in Gajraula motivates researchers to achieve their goals for research. Owing to our dedication, SVU has received grants from GOI for research on areas of national importance. The faculty members provide guidance to the scholars until they achieve their aim. We have set up the incubation center to provide fillip to new ideas that foster entrepreneurship. We want to be an institution that supports the ‘Make in India’ vision of the government. The center supports new ideas that enable the young entrepreneurs to create startups & become successful. Under the strong leadership of Dr. Sudhir Giri, till date we have successfully incubated 150 start-ups. This speaks of our exemplary education & make us the Best Private University in Uttar Pradesh (UP). These startups are not only creating wealth but also providing employment to the needy. The industrialists have lamented that the epicenter for entrepreneurship will be the educational institutions. We need to provide them with the support & infrastructure for this. The annual hackathon attracts individuals who showcase their business acumen to make us the Best Private University in Moradabad. SVU has a dedicated International Research & collaboration Cell (IRCC) that collaborates with universities abroad. Faculty & students who want to pursue studies abroad the IRCC starts admission formalities for them. We have partnered with reputed institutions for providing excellent research collaborations. Those who wish to do P. HD abroad the IRCC help them gain admission & make us the Top University in India. A lot of our faculty members are pursuing their research internationally & contributing to the welfare of humanity. SVU strives to make our students feel comfortable at the campus. Separate hostel for boys & girls with 24 hour security is available at SVU. The cafeteria serves nutritious food to the students. Gym, recreation hall & the sports ground help to relax our students & make us the Best University in Uttar Pradesh (UP). The campus has an in house ATM & convenience store for the benefit of the students. SVU enables placement through exemplary training. We train on communication & interpersonal skills in order to refine the personality of the students. We make them practice mock interviews & group discussion that help to clear placement tests. Ninety percent of the students get placed before their last semester to make us the best university in Moradabad. We have hired industrial trainers in order to provide training on block chain, machine learning, artificial intelligence (AI), and python & data science. These trainers have years of experience that enables them in training the students. The students gain key insights on these technologies & sharpen their acumen to make us the Best University in Gajraula.

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