✍ 20 Essential AI Abbreviations to Know Understanding these abbreviations will help you read and comprehend AI literature more effectively. Machine Learning (ML) 🤖 AI - Artificial Intelligence 🧠 ML - Machine Learning 🔍 DL - Deep Learning 🎮 RL - Reinforcement Learning 📈 SVM - Support Vector Machine Data Science 🔍 EDA - Exploratory Data Analysis 📊 PCA - Principal Component Analysis 📉 ROC - Receiver Operating Characteristic 📏 AUC - Area Under the Curve 📐 MSE - Mean Squared Error Natural Language Processing (NLP) 🗣️ NLP - Natural Language Processing 🏷️ NER - Named Entity Recognition 📝 POS - Part of Speech 🔄 BERT - Bidirectional Encoder Representations from Transformers 🔁 RNN - Recurrent Neural Network Computer Vision 👁️ CNN - Convolutional Neural Network 🌀 GAN - Generative Adversarial Network 📄 OCR - Optical Character Recognition 🌐 AR - Augmented Reality 🔲 IoU - Intersection over Union
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I appreciate the work that goes into the hype cycles. Period. Let me (without being asked by anyone) point some things out: >The visual makes development look linear. It's not. >The visual makes it look like each tech development area is discrete and isolated. They're not. >The visual makes it seem like its inevitable that a technology move though these phases. It's not. One of the reasons that "AI" is so prominent now is because a very clever UI was added to a combination of machine learning (ML), deep learning, neural networks, natural language processing (NLP), reinforcement learning, and computer vision. So if we look at just the one dot of "generative AI" on the hype cycle, we need to be able to uncover the components that make up that one dot and understand the forces at work on all those components (both tech and business). The other dynamic at work is that advances in one or more components that make up one of those dots are also feeding advances in other technologies and all those can add up to something that moves quickly and in a non-linear fashion. So this hype cycle is a nice table of contents maybe but by no means is it the whole story.
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a map Ai 1- Introduction to GenAI 2- Human Decision Making and its Biases 3- Structured Approach to Problem Solving 4- Applied Machine Learning 5- Getting Started with Large Language 6- Introduction to Natural Language Processing (NLP) 7- Exploring NLP using Deep Learning 8- Getting Started with Deep Learning Models 9- Building LLM Apps using Prompt Engineering 10- Building End-to-End Generative AI Application 11- Building Production ready RAG Systems using LlamaIndex 12- Finetuning LLMs 13- Training LLMs from Scratch 14- Getting Started with Stable Diffusion 15- Mastering Methods and Tools of Stable Diffusion 16- Advanced Stable Diffusion Techniques 17- Responsible AI in the Generative AI Era
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Software & Website development , App development and Digital marketing company in India & USA, Work in globally.
🖥 AI and Machine Learning 👇 Artificial Intelligence (AI) : AI refers to the simulation of human intelligence in computers or machines. It involves the development of systems or software that can perform tasks such as problem-solving, reasoning, understanding natural language, recognizing patterns, and making decisions. 1. Expert Systems : AI systems that mimic human expertise in a specific domain. 2. Machine Learning : A subset of AI that focuses on building algorithms that enable computers to learn from data. 3. Neural Networks : Models inspired by the human brain, used for tasks like image and speech recognition. 4. Natural Language Processing (NLP) : Techniques to understand and generate human language. 5. Computer Vision : The field of AI that deals with enabling computers to interpret and understand visual information from the world. Machine Learning (ML): Machine Learning is a subset of AI that focuses on developing algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed. ML systems use statistical techniques to improve their performance on a specific task over time as they are exposed to more data. 1. Data : ML algorithms require data to learn patterns and make predictions. This data can be structured or unstructured. 2. Features : These are the variables or characteristics extracted from the data that the ML algorithm uses for making predictions. 3. Supervised Learning : A type of ML where the algorithm is trained on labeled data, making predictions and learning from known outcomes. 4. Reinforcement Learning : A type of ML where agents learn by interacting with an environment and receiving feedback in the form of rewards or penalties. 5. Deep Learning : A subfield of ML that involves artificial neural networks with many layers, used for tasks like image and speech recognition. #ArtificialIntelligence #MachineLearning #Computer #AI #AIApplications #sunshineitsolution #itcompany #indoreitcompany #itdevelopmentcompany #sunshineworlwide #sunshineindore
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I am thrilled to share that I have completed the AI Fundamentals certificate from IBM SkillsBuild! This course has been a rewarding journey that has provided me with a solid conceptual understanding in various crucial aspects of Artificial Intelligence, such as: 🌐 IA model: I also gained an understanding of how to run an AI model using IBM Watson Studio. 🤖 Natural Language Processing (NLP): The ability of machines to understand and respond to human language. 👾 Computer Vision: Enabling computers to 'see' and interpret the visual world similarly to humans. 👨💻 Machine Learning and Deep Learning: Techniques that allow machines to learn from data and improve with experience. 🔮 Chatbots and Artificial Neural Networks: Essential tools and frameworks for building interactive and efficient AI systems. Furthermore, I delved into AI ethics and explored how these concepts can be applied in the real world to solve practical challenges. #DataScience #DataAnalyst #AI Thanks IBM! I love IBM SkillsBuild.
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Senior Engineering and Technology GCC Leader at Ford Business Solutions | Product Development | Driving Digital Transformation
🔍 Exploring the World of Data Science and Artificial Intelligence Data Science and Artificial Intelligence (AI) are reshaping industries and driving innovation at an unprecedented pace. 🚀 Let's dive into some fundamental concepts that power these transformative fields: Data is King: Data is the lifeblood of data science and AI. The more high-quality data we have, the better our models can learn and make predictions. Machine Learning: This subset of AI focuses on creating algorithms that can learn from data and make predictions or decisions without explicit programming. Think recommendation systems, image recognition, and more. Deep Learning: A subset of machine learning, deep learning uses neural networks inspired by the human brain to tackle complex tasks like natural language processing and computer vision. Natural Language Processing (NLP): NLP enables machines to understand and generate human language, opening doors to chatbots, sentiment analysis, and more. 🔗 These concepts represent the building blocks of the exciting world of Data Science and AI. They're not just buzzwords but the tools and methodologies that empower us to make data-driven decisions, create intelligent systems, and unlock new possibilities. What are your favorite data science and AI concepts, and how do you see them shaping the future? Let's keep the conversation going and continue to learn and grow together! 💬 #DataScience #ArtificialIntelligence #MachineLearning #AI #NLP #EthicsInAI #LinkedIn Brenda Mendes
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Very useful to guide people willing to know better what is behind AI.
Ph.D. Candidate specializing in AI/ML Research | Scientific Reviewer | Academic Writer | Data Science | Expertise in AI・Machine Learning・Deep Learning・NLP・Computer Vision・Medical Data Analysis
𝗟𝗶𝘀𝘁 𝗼𝗳 𝗔𝗜 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀! From machine learning to deep learning, natural language processing (NLP), and reinforcement learning, the realm of AI is brimming with innovation and possibilities. These algorithms are powering incredible advancements across various fields. Let's explore some key players: 1-4 1. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟) - The foundation for many AI applications. It uses techniques like supervised learning (training models with labeled data, like spam filters) and unsupervised learning (finding patterns in unlabeled data, like customer segmentation). Ensemble methods like bagging and boosting combine multiple models for even better performance. 2. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟) - Takes ML a step further with artificial neural networks, inspired by the human brain. Convolutional Neural Networks (CNNs) excel at image recognition (like facial recognition in photos). Recurrent Neural Networks (RNNs) handle sequential data well (like machine translation). Generative Adversarial Networks (GANs) can even create realistic images. 3. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗥𝗟) - Allows algorithms to learn through trial and error, interacting with an environment. This paves the way for autonomous systems like self-driving cars and decision-making agents that can adapt to changing situations. 4. 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (NLP) - bridges the gap between computers and human language. It uses techniques like text analysis and machine learning to unlock the meaning behind words. This allows computers to understand sentiment, translate languages, and even generate human-like text, all of which are powering innovations in areas like communication, customer service, and content creation. 🎯 A high-quality image of the List of AI algorithms can be found in the comments! #phd #phdlife #phdthesis #thesisviva #academics #thesisdefense #research #researchpaper #researchdefense #academicwriting #ai #machinelearning #deeplearning #genai #ann #publication #nlp
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This is a really nice introduction to natural language processing #nlp. Language models are useful for a variety of tasks, including: 📝 speech recognition 📝 machine translation 📝 natural language generation 📝 optical character recognition 📝 handwriting recognition 📝 grammar induction 📝 information retrieval Large language #models, currently their most advanced form, are a combination of larger #datasets, #feedforward neural networks, and transformers. They have superseded recurrent #neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model. #deepneuralnetworks #chatgpt #ai #artificialintelligence #neuralnetwork #ai #programming #python #statistics #datamining
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This is a really nice introduction to natural language processing #nlp. Language models are useful for a variety of tasks, including: 📝 speech recognition 📝 machine translation 📝 natural language generation 📝 optical character recognition 📝 handwriting recognition 📝 grammar induction 📝 information retrieval Large language #models, currently their most advanced form, are a combination of larger #datasets, #feedforward neural networks, and transformers. They have superseded recurrent #neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model. #deepneuralnetworks #chatgpt #ai #artificialintelligence #neuralnetwork #ai #programming #python #statistics #datamining
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Beta Microsoft Learn Student Ambassador |Event manager of Microsoft tech hub | Graphic Designer | former intern @ PTV center | C# Developer | joint secretary of ITSSociety| IT Student
🙌 Unlock the Power of AI: Dive into Natural Language Processing with TensorFlow! 🧠💻 Curious about how machines understand human language? 🌐 Explore our comprehensive module on Natural Language Processing (NLP) using TensorFlow, and master the art of creating intelligent systems that can interpret and respond to text like a human. 📚 Why TensorFlow? 🤔 ✨ Versatility: From research to production, TensorFlow supports various machine learning models and neural networks. ✨ Flexibility: Easily deployable on different platforms like CPUs, GPUs, and TPUs. ✨ Community Support: Extensive documentation, tutorials, and a vibrant community. Projects You Can Build: 🚀 🤖 Chatbots: Create intelligent chatbots that can handle customer queries. 📧 Text Classification: Build systems for spam detection or sentiment analysis. 🌐 Language Translation: Develop models that translate languages with high accuracy. 🎙️ Voice Recognition: Implement voice-activated systems and personal assistants. Ideal for Your FYP (Final Year Project): 🎓 💡 Innovative Solutions: Stand out with cutting-edge AI solutions. 🛠️ Practical Applications: Address real-world problems with advanced NLP models. 📈 Skill Enhancement: Gain hands-on experience with one of the most popular AI frameworks. 🔗 Access the Module Now https://lnkd.in/dnRfp5ZV #AI #MachineLearning #TensorFlow #NLP #ArtificialIntelligence #TechEducation #LearnToCode
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🚀 Ready to master Artificial Intelligence in just 10 days? 🧠 Here's your roadmap to AI expertise! Day 1: Dive into the world of AI - Understand what AI is and explore its vast applications. 🌐 Day 2-3: Get the fundamentals of Machine Learning - Learn about data, features, algorithms, and more. 🤖 Day 4-5: Go deep into Deep Learning - Explore neural networks and popular frameworks like TensorFlow and PyTorch. 🤯 Day 6: Unravel Natural Language Processing (NLP) - Discover tokenization, sentiment analysis, and named entity recognition. 📚 Day 7: Delve into Computer Vision - Learn image recognition, object detection, and convolutional neural networks. 📷 Day 8: Ethical AI - Understand the importance of ethics and tackle bias in AI algorithms. 🤝 Day 9: Tool Time - Master AI development tools, platforms, datasets, and APIs. 🧰 Day 10: Your AI Project - Build a chatbot, image classifier, or dive into data analysis with AI techniques. Get hands-on! 🤖🚀 Throughout your journey, remember to practice and participate in AI communities. Enhance your knowledge and get ready to embrace the world of Artificial Intelligence! Learning AI is a rewarding adventure, so enjoy every bit of it. 👍📚 #ArtificialIntelligence #AI #LearningJourney
