Artificial Intelligence & ChatGPT Prompts

WhatsApp is no longer a platform just for chat. It's an educational goldmine. If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners. I have curated the list of best WhatsApp channels to learn coding & data science for FREE Free Courses with Certificate 👇👇 https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g Jobs & Internship Opportunities 👇👇 https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 Web Development 👇👇 https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z Python Free Books & Projects 👇👇 https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Java Free Resources 👇👇 https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s Coding Interviews 👇👇 https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X SQL For Data Analysis 👇👇 https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v Power BI Resources 👇👇 https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c Programming Free Resources 👇👇 https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17 Data Science Projects 👇👇 https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y Learn Data Science & Machine Learning 👇👇 https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D ENJOY LEARNING 👍👍

Why open-source AI models are good for the world Open innovation lies at the heart of the artificial-intelligence (ai) boom. The neural network “transformer”—the t in GPT—that underpins OpenAI’s was first published as research by engineers at Google. TensorFlow and PyTorch, used to build those neural networks, were created by Google and Meta, respectively, and shared with the world. Today, some argue that AI is too important and sensitive to be available to everyone, everywhere. Models that are “open-source”—ie, that make underlying code available to all, to remix and reuse as they please—are often seen as dangerous.

Top 10 Web Development Technologies 🌐 1. 🟨 JavaScript — 98% usage 2. 🔵 TypeScript — 78% adoption 3. 🟢 Node.js — 75% backend choice 4. ⚛️ React — 70% frontend framework 5. 🅰️ Angular — 55% enterprise use 6. 💚 Vue.js — 49% growing popularity 7. 🐍 Python — 48% for full-stack 8. 💎 Ruby on Rails — 45% rapid development 9. 🐘 PHP — 43% widespread use 10. ☕ Java — 40% enterprise solutions

𝐇𝐞𝐥𝐥𝐨 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞👋, I’m excited to share an incredible opportunity with you!  Join our 𝐅𝐑𝐄𝐄 𝐎𝐧𝐥𝐢𝐧𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬 𝐨𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 😍 and explore the pathway to becoming a successful Data Scientist. 𝐄𝐱𝐜𝐥𝐮𝐬𝐢𝐯𝐞 𝐎𝐟𝐟𝐞𝐫 :- Attendees will receive free bonuses valued at INR 5,000!🤗 Eligibility :- Students,Freshers & Working Professionals 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:- https://bit.ly/4feAJwh Note: Limited slots are available—register soon! 𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞:- November 9, 2024, at 7 PM Don’t miss out on this valuable learning experience!🏃‍♂️

Hello Guys😍, As you know AI&ML is highly demanding now-a-days. So we have brought you a great opportunity  𝐅𝐑𝐄𝐄 𝐎𝐧𝐥𝐢𝐧𝐞 Masterclass 𝐎𝐧 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠  Know The Roadmap To Become an AI and ML Engineer  Eligibility :- Students , Freshers & Working Professionals  𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-  https://bit.ly/3UtUojt ( Limited Slots🏃‍♂️ ) Date & Time:- November 7, 2024,  7 PM Don't Miss This Opportunity 🤗

Software Engineers vs AI Engineers: 👊 Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise. 1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year. 2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job. The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights. Here is an example. Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀 Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023. In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."

12 Essential Math Theories for AI Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge: Curse of Dimensionality: Challenges with high-dimensional data. Law of Large Numbers: Reliability improves with larger datasets. Central Limit Theorem: Sample means approach a normal distribution. Bayes' Theorem: Updates probabilities with new data. Overfitting & Underfitting: Finding balance in model complexity. Gradient Descent: Optimizes model performance. Information Theory: Efficient data compression. Markov Decision Processes: Models for decision-making. Game Theory: Insights on agent interactions. Statistical Learning Theory: Basis for prediction models. Hebbian Theory: Neural networks learning principles. Convolution: Image processing in AI. Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.

Programming is no longer about how well you google search. Programming is now about how well you can write prompts for an AI system to generate code for you, and you validate it.

12 Fundamental Math Theories Needed to Understand AI 1. Curse of Dimensionality This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data. 2. Law of Large Numbers A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods. 3. Central Limit Theorem This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning. 4. Bayes’ Theorem A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI. 5. Overfitting and Underfitting Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance. 6. Gradient Descent This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models. 7. Information Theory Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency. 8. Markov Decision Processes (MDP) MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents. 9. Game Theory Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments. 10. Statistical Learning Theory This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions. 11. Hebbian Theory This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory. 12. Convolution (Kernel) Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.

Get Free Resources to learn stock marketing 👇👇 https://chat.whatsapp.com/Jxasfs1mMJUFZ5fBEvfs9o

Hey guys 👋 Since many of you requested for data analytics recorded video lectures, here you go! 👇👇 https://topmate.io/analyst/1068350 It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge. Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your data analytics journey... All the best!👍✌️

Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻 It's easy with Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches. ⚡️ Place your ad here in three simple steps: 1 Sign up 2 Top up the balance in a convenient way 3 Create your advertising post If your ad aligns with our content, we’ll gladly publish it. Start your promotion journey now!

SOME USEFUL  WEBSITES  ONLINE EDUCATIONAL SUPPORT www.khanacademy.org www.academicearths.org www.coursera.com www.edx.org www.open2study.com www.academicjournals.org codeacademy.org youtube.com/education BOOK SITES www.bookboon.com http://ebookee.org http://sharebookfree.com http://m.freebooks.com www.obooko.com www.manybooks.net www.epubbud.com www.bookyards.com www.getfreeebooks.com http://freecomputerbooks.com www.essays.se www.sparknotes.com www.pink.monkey.com ANSWERS TO QUESTIONS www.ehow.com www.whatis.com www.howstuffwork.com www.webopedia.com www.plagtracker.com www.answers.com SEARCH SITES ■ About.com (www.about.com) ■ AllTheWeb (www.alltheweb.com) ■ AltaVista (www.altavista.com) ■ Ask Jeeves! (www.askjeeves.com) ■ Excite (www.excite.com) ■ HotBot (www.hotbot.com) ■ LookSmart (www.looksmart.com) ■ Lycos (www.lycos.com) ■ Open Directory (www.dmoz.org) ■ Google (www.google.com) ■ Mamma (www.mamma.com) ■ Webcrawler (www.webcrawler.com) ■ Aol (www.aol.com) ■ Dogpile (www.dogpile.com) ■ 10pht (www.10pht.com) SEARCHING FOR PEOPLE ■ AnyWho (www.anywho.com) ■ InfoSpace (www.infospace.com) ■ Switchboard (www.switchboard.com) ■ WhitePages.com (www.whitepages.com) ■ WhoWhere (www.whowhere.lycos.com) SEARCHING FOR THE LATEST NEWS ■ ABC News (www.abcnews.com) ■ CBS News (www.cbsnews.com) ■ CNN (www.cnn.com) ■ Fox News (www.foxnews.com) ■ MSNBC (www.msnbc.com) ■ New York Times (www.nytimes.com) ■ USA Today (www.usatoday.com) SEARCHING FOR SPORTS HEADLINES AND SCORES ■ CBS SportsLine (www.sportsline.com) ■ CNN/Sports Illustrated (sportsillustrated.cnn.com) ■ ESPN.com (espn.go.com) ■ FOXSports (foxsports.lycos.com) ■ NBC Sports (www.nbcsports.com) ■ The Sporting News (www.sportingnews.com) SEARCHING FOR MEDICAL INFORMATION ■ healthAtoZ.com (www.healthatoz.com) ■ kidsDoctor (www.kidsdoctor.com) ■ MedExplorer (www.medexplorer.com) ■ MedicineNet (www.medicinenet.com) ■ National Library of Medicine (www.nlm.nih.gov) ■ Planet Wellness (www.planetwellness.com) ■ WebMD Health (my.webmd.com)

😂😂

Hey Guys👋, The Average Salary Of a Data Scientist is 14LPA  𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍 We help you master the required skills. Learn by doing, build Industry level projects 👩‍🎓 1500+ Students Placed 💼 7.2 LPA Avg. Package 💰 41 LPA Highest Package 🤝 450+ Hiring Partners Apply for FREE👇 : https://bit.ly/3ZI4CQY ( Limited Slots )

YouTube is FREE. ChatGPT is FREE. Podcasts are FREE. Google Gemini is FREE. Udemy is FREE The internet is free education. Start learning, building, and earning.