Muhammad Kamran Blouch

Dubai is going to hold 𝗚𝗟𝗢𝗕𝗔𝗟 𝗣𝗥𝗢𝗠𝗣𝗧 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 𝗖𝗛𝗔𝗠𝗣𝗜𝗢𝗡𝗦𝗛𝗜𝗣, the world’s largest AI prompt competition, at Dubai's Museum of the Future on 20th-21st May 2024 offering prizes totaling 𝗗𝗵𝟭 𝗠𝗜𝗟𝗟𝗜𝗢𝗡 to winners! Prompt engineering has emerged as a critical skill in light of the recent surge in #generativeAI applications such as #ChatGPT and #Midjourney. The competition evaluates participants’ proficiency in innovating AI prompts through generative AI applications across three categories: Coding, Art, and Literature. Ten finalists in each of the three categories will advance to compete in Dubai where evaluation is made by a jury comprising academics, innovators, and technology and AI experts based on criteria such as the speed, quality, and accuracy of their content. The #promptengineering challenge according to 𝗦𝗛𝗘𝗜𝗞𝗛 𝗛𝗔𝗠𝗗𝗔𝗡 𝗕𝗜𝗡 𝗠𝗢𝗛𝗔𝗠𝗠𝗘𝗗 𝗕𝗜𝗡 𝗥𝗔𝗦𝗛𝗜𝗗 𝗔𝗟 𝗠𝗔𝗞𝗧𝗢𝗨𝗠 -Crown Prince of Dubai and Chairman of the Board of Trustees of the Dubai Future Foundation- “is aligned with Dubai’s efforts to become a destination for leading talent and a platform for Generative AI startups”. Register at: challenge.dub.ai 𝙎𝙚𝙚 𝙢𝙮 𝙛𝙪𝙩𝙪𝙧𝙚 𝙥𝙤𝙨𝙩𝙨 𝙤𝙣 𝘼𝙄 𝙖𝙣𝙙 𝘽𝙪𝙨𝙞𝙣𝙚𝙨𝙨. 𝘾𝙡𝙞𝙘𝙠 𝙤𝙣 🔔 𝙤𝙣 𝙢𝙮 𝙥𝙧𝙤𝙛𝙞𝙡𝙚 𝙥𝙖𝙜𝙚. #BusinessAnalysis

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5 of the most popular NLP Applications (part1): Natural Language Processing (NLP) is a field within Artificial Intelligence (AI) that focuses on enabling machines to understand, interpret, and generate human language. From analyzing social media sentiments to powering virtual assistants like Siri and Alexa, NLP plays a crucial role in how we interact with technology. You can think about NLP as a bridge between computers and human language! Some of the most popular applications in the NLP field are: 1. Text Classification: Categorizing Information It involves assigning predefined categories to text. Examples of Text Classification are: Email spam detection and Topic classification (tagging articles as sports, politics, or entertainment). These Applications are done using models like Logistic Regression (the traditional way), or by using Transformers models like BERT. 2. Named Entity Recognition (NER): Finding Key Information It identifies and labels specific entities within a text, such as names, dates, organizations, and locations. Examples of NER are: Extracting customers names and complaint details from support tickets, and Identifying medical terms in healthcare documents. To achive such tasks, we can use CRFs (Conditional Random Fields) for simpler tasks, or Fine-tuned transformers like spaCy's NER pipeline and Hugging Face models for more complicated tasks! 3. Sentiment Analysis: Understanding Emotions in Text This application focuses on determining the tone or emotion behind a piece of text. Examples of this applications is analyzing customer feedback to improve services, and maybe monitoring social media comments and hide offensive comments. Models used here are: LSTMs (Long Short-Term Memory networks) for sequence-based analysis, and Transformers models for deeper contextual understanding. 4. Question Answering (QA): Giving Precise Answers As the name suggests, QA systems extract or generate answers to user queries based on a dataset or context. Examples are simple chatbots that are used in customer service. Models for this application can be as smaller as BERT, or as bigger as Large Language Models (such as GPTs!) 5. Machine Translation: Breaking Language Barriers This application enables automatic translation between languages. Examples are for sure Google Translate, and even translating websites for global audiences. You can do so using Sequence-to-sequence models with attention mechanisms (e.g., Transformer), and Multilingual LLMs like mBERT or M2M-100. follow Saad Shahrour and Raizer If you want to learn more about AI and NLP!

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The Artificial Intelligence market in India is projected to grow by 28.63% (2024-2030), resulting in a market volume of US$28.36bn in 2030 as per Statista report. AI is booming, and India is doing its part to take it to the next level with INDIAai. #ai #indiaai #genai #adoption https://lnkd.in/dpUebBT6

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The best RAG analogy you'll find in the internet: “RAG is to LLMs what an open-book exam is to students.” Imagine GPT-4 is a very smart law student. But he didn’t prepare for the exam. He has some general knowledge about law, But lacks the specific knowledge for the exam. GPT-4 alone would “lie” a lot during the exam (aka hallucinations). Luckily, he can bring books. Many books. The books have answers. But how to quickly find the answers if there are 1000s of pages? That’s where the “retrieval” part comes in. RAG stands for Retrieval Augmented Generations. Retrieval is a “magical” search. It uses Vector Databases to find relevant information. So during the exam, our student would see the question. And the “retrieval” part will help him to quickly find: ↳ the right books ↳ the relevant chapters ↳ the relevant paragraphs The student would quickly: → read the relevant information → write the answers based on the information That’s how GPT-4 works in RAG applications. — RAG is a smart student with books (and a “magical” search). RAG = LLMs + Retrieval (from Vector Databases) 🔔 My name is Kris. I’m an AI Engineer. I love learning and teaching about AI. Make sure to follow Kris Ograbek if you like AI! 🔔 PS. Did the analogy help you understand how RAG works? #AIEngineering

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🚀 First Lecture on Deep Learning with Computer Vision 🖥️📊 ✨ Hands-On Experience with PyTorch & CIFAR10 Dataset! Dive into the world of Deep Learning and Computer Vision with Muhammad Abdullah, as I kickstart an exciting journey! 🌟 Here's a glimpse of what we covered in our first session: 🔹 Data Preparation: Importing and normalizing the CIFAR10 dataset with PyTorch to make it training-ready. 🔹 Building a CNN: A 3-layer convolutional neural network with ReLU activation to classify images across 10 categories like 🐱 Cat, 🚗 Automobile, and ✈️ Airplane. 🔹 Training the Model: Leveraging GPU acceleration for faster convergence and achieving impressive accuracy. 🔹 Model Evaluation: Showcasing predictions with confidence levels and visualizations for enhanced clarity. 🔹 Real-World Application: Implemented object detection on custom images and split-class analysis for cats and dogs. 🐶🐱 💡 Why Does This Matter? Deep Learning is revolutionizing industries—from e-commerce to healthcare. Through this lecture, I aim to simplify complex concepts and showcase how to bring real-world AI projects to life. 🔥 Special Thanks I’d like to extend my gratitude to Syed Karar Haider Bukhari for guiding us through this journey. Your mentorship is invaluable! 🙌 🔥 Want to Learn or Collaborate? Whether you're a professional, student, or business owner, I can help you: ✔️ Build AI-powered solutions ✔️ Train models for specific use cases ✔️ Visualize and interpret data insights 🌐 Let's connect and create something extraordinary together! 💬 Drop your thoughts or DM me to discuss opportunities. #DeepLearning #ComputerVision #PyTorch #AI #MachineLearning #TechEducation #TechlytixLabs #Innovation

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𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗲:  𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 Here’s the breakdown between Data Engineers, ML Engineers, and Data Scientists, shedding light on the unique skill sets, responsibilities, and the collaborative dance that propels innovation in the realm of data. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿: •The foundational role of a Data Engineer in crafting robust data pipelines and infrastructure. •The magic touch of an ML Engineer in transforming data into machine learning models that power intelligent applications. •The analytical prowess of a Data Scientist in extracting actionable insights and driving data-driven decision-making. 🌐 Whether you're charting your career path or aiming to build high-impact data teams, understanding these roles is pivotal. Join the conversation, share your insights, and let's unravel the mysteries of the data-driven world together! 🚀  𝗨𝗻𝗹𝗼𝗰𝗸 𝗠𝗼𝗿𝗲 𝗔𝗜 𝗚𝗼𝗼𝗱𝗶𝗲𝘀! If this content helps, repost this ♻️ to your network and follow Dirk Zee. - - - - -  📌 Want a high-res PDF of this Cheat Sheet? ♻ Repost and like this post & subscribe to my free newsletter (link in bio). You'll get this cheat sheet sent to you + 50 more + my ebook for free.

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𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗲:  𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 Here’s the breakdown between Data Engineers, ML Engineers, and Data Scientists, shedding light on the unique skill sets, responsibilities, and the collaborative dance that propels innovation in the realm of data. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿: •The foundational role of a Data Engineer in crafting robust data pipelines and infrastructure. •The magic touch of an ML Engineer in transforming data into machine learning models that power intelligent applications. •The analytical prowess of a Data Scientist in extracting actionable insights and driving data-driven decision-making. 🌐 Whether you're charting your career path or aiming to build high-impact data teams, understanding these roles is pivotal. Join the conversation, share your insights, and let's unravel the mysteries of the data-driven world together! 🚀  𝗨𝗻𝗹𝗼𝗰𝗸 𝗠𝗼𝗿𝗲 𝗔𝗜 𝗚𝗼𝗼𝗱𝗶𝗲𝘀! If this content helps, repost this ♻️ to your network and follow Dirk Zee. - - - - -  📌 Want a high-res PDF of this Cheat Sheet? ♻️ Repost and like this post & subscribe to my free newsletter: (link in bio) You'll get this cheat sheet sent to you + 50 more + my ebook for free.

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𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗲:  𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 Here’s the breakdown between Data Engineers, ML Engineers, and Data Scientists, shedding light on the unique skill sets, responsibilities, and the collaborative dance that propels innovation in the realm of data. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿: •The foundational role of a Data Engineer in crafting robust data pipelines and infrastructure. •The magic touch of an ML Engineer in transforming data into machine learning models that power intelligent applications. •The analytical prowess of a Data Scientist in extracting actionable insights and driving data-driven decision-making. 🌐 Whether you're charting your career path or aiming to build high-impact data teams, understanding these roles is pivotal. Join the conversation, share your insights, and let's unravel the mysteries of the data-driven world together! 🚀  𝗨𝗻𝗹𝗼𝗰𝗸 𝗠𝗼𝗿𝗲 𝗔𝗜 𝗚𝗼𝗼𝗱𝗶𝗲𝘀! If this content helps, repost this ♻️ to your network and follow Dirk Zee. - - - - -  📌 Want a high-res PDF of this Cheat Sheet? ♻️ Repost and like this post & subscribe to my free newsletter: (link in bio) You'll get this cheat sheet sent to you + 50 more + my ebook for free.

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Do you know about Word Embeddings in NLP ? When dealing with text data in NLP ,word embeddings plays crucial role. Word embedding converts words to its numerical vector representation. Here’s a list of popular embedding techniques in NLP with a brief description: One-Hot Encoding: Represents words as binary vectors, where only one element is 1, and the rest are 0. TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on their frequency in a document and their inverse frequency across all documents, highlighting important terms. Word2Vec: A predictive model that generates word embeddings by learning word associations based on context (using CBOW or Skip-gram architectures). GloVe (Global Vectors for Word Representation): A count-based model that creates word embeddings by analyzing the global co-occurrence matrix of words in a corpus. FastText: An extension of Word2Vec that incorporates subword information (character n-grams), allowing it to handle out-of-vocabulary words better. ELMo (Embeddings from Language Models): Contextual word embeddings generated from a deep bidirectional LSTM, where embeddings change based on the context of the sentence. BERT (Bidirectional Encoder Representations from Transformers): Contextual embeddings generated using a transformer-based model that understands the context of a word in both directions of the sentence. Sentence-BERT: An adaptation of BERT fine-tuned to produce embeddings for entire sentences rather than individual words. Transformer Embeddings: Embeddings derived from the transformer architecture using attention mechanisms to encode the context of a word in parallel for the entire sequence. Below are Major Area's of study to get expertise in NLP in sequence: RNN -> LSTM RNN -> GRU (Gated Recurrent Units) -> Encoder-Decoder Architecture (Seq2Seq) -> TRANSFORMERS -> BERT -> GPT(LLMs) Follow this link to join my WhatsApp group: https://lnkd.in/gkn9iFXe  #NLP #KnowledgeSharing

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"Understanding the Basics of Probability for ML: A Beginner’s Journey" Machine learning is not just about algorithms; it is deeply rooted in the mathematics of uncertainty and decision-making. That is why today, in our Foundations for Machine Learning course, we explored the fundamentals of probability—a critical building block for any serious machine learning engineer. This lecture was designed specifically for beginners, focusing on concepts that might seem simple at first but are essential for building robust ML models. The lecture is live on Vizuara's YouTube channel: https://lnkd.in/gEYR6YnP Here is what we cover: Key Takeaways: 1) Conditional Probability: The cornerstone of decision-making in machine learning. For example, if an email contains certain words, what is the probability it is spam? This principle forms the basis of spam filters, recommendation systems, and many classification algorithms. 2) Bayes’ Theorem: A powerful tool for updating probabilities as new data becomes available. Though the formula may seem complex initially, we broke it down with real-world examples, including designing a spam filter for the infamous "Nigerian Prince" emails. Core Probability Rules: -Addition Rule: For understanding "or" probabilities (e.g., rolling an even number or a number greater than 4 on a dice). -Multiplication Rule: For "and" probabilities (e.g., drawing two aces consecutively from a deck of cards). -Complement Rule: To calculate probabilities of complementary events (e.g., the probability of at least one head in two coin tosses). -Intuitive Visualization: Using Venn diagrams and logical reasoning to derive formulas, making abstract concepts more relatable. Why This Matters: In the real world, uncertainty is the norm. Whether you are developing a medical diagnosis tool or a recommendation engine, understanding probabilities enables you to quantify and handle uncertainties effectively. Moreover, many traditional ML models, like the Naive Bayes Classifier, are built directly on these principles. What is Next: In the next lecture, we will take a deeper dive into Bayes’ Theorem, exploring its applications with detailed examples. This foundation will prepare you for building and interpreting ML models with greater confidence and clarity. If you have been following along, you are making great progress toward mastering the math behind machine learning. It is not about memorizing formulas but about building intuition—and that is exactly what we are doing together. Let us continue this journey, one concept at a time!

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By leveraging deep learning technologies, AI systems can achieve higher accuracy, handle larger datasets, adapt to various applications, and automate complex tasks, thereby driving advancements in numerous fields. Explore axiusSoftware's offerings in AI space. #axiusSoftware #AI #digitaltransformation #deeplearning #ML

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Retrieval-Augmented Generation (RAG) 🚀 ✨ What is Retrieval-Augmented Generation? Retrieval-Augmented Generation (RAG) is a cutting-edge AI technology that enhances generative models by integrating them with external knowledge retrieval. This integration helps the AI generate more accurate, context-aware, and informed responses, leveraging the power of vast datasets. 🔍 Why RAG? In today’s data-driven era, efficiently mining and utilising vast information repositories is crucial. RAG significantly improves how machines understand and interact with data, making it invaluable for businesses, researchers, and developers who need AI to produce actionable insights and informed decisions. 🛠 How Does RAG Work? 1. Embedding and Vector Databases: RAG uses advanced embedding techniques to convert text into numerical vectors, which are then stored in vector databases. This process facilitates the quick retrieval of relevant information based on similarity in a high-dimensional space. 2. Data Retrieval: Leveraging the power of vector databases, RAG efficiently retrieves the most relevant data from a large knowledge base in response to a query. 3. Content Generation: Using the retrieved data, the generative model crafts responses that are informed, accurate, and contextually relevant. 4. Integration: The final output is a blend of retrieved data and generated content, producing a coherent and contextually rich response. 🌟 By combining the prowess of embeddings, vector databases, and generative models, RAG is setting new benchmarks for AI applications across industries—from enhancing chatbots and research tools to creating responsive AI assistants and more. Image credit: Rocky Bhatia #learning #softwaredevelopment #tech #database #servers

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📌 AI in Banking – How Artificial Intelligence is Used in Banks Banks have historically been at the forefront of #technological advancements, they are renowned for using computers as well as providing internet-based #financialservices. However, the rise of #AI has brought with it a new dawn of innovations. These days, #artificialintelligence (AI) is disrupting the entire banking sector in several ways. These #technologies range from customer support through chatbots to assisting in deterring complex #frauds in the industry. Their #innovations have enabled banks to provide customized solutions, operate more efficiently, and minimize risks better when compared with #conventional methods. Recent industry reports suggest the global #AI in #banking market size stood at $3.88 billion in 2022 and this figure is projected to hit $64.03 billion in 2030 at a CAGR of 32.6% from 2021 to 2030. Therefore, the use of #AI in banking continues to expand and introduce new vistas while reshaping financial services. Feeling curious to know how AI in banking is used? This blog post deals with how #AI works in the banking sector and its impact on revolutionizing #finance generally. 🔗 https://lnkd.in/g-FWfWce #financialanalysis #banking #ai #financialservices

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🚀 Exploring Types of Retrieval-Augmented Generation (RAG) in NLP 🚀 Retrieval-Augmented Generation (RAG) is revolutionizing the field of Natural Language Processing (NLP) by enhancing the capabilities of Large Language Models (LLMs). Here’s a concise overview of the three main types of RAG, based on recent research¹: 1. Naive RAG: This basic form integrates external knowledge by retrieving relevant documents and generating responses based on them. It’s a straightforward approach but can be limited by the quality of retrieved documents. 2. Advanced RAG: This type improves upon Naive RAG by incorporating more sophisticated retrieval mechanisms and better integration techniques, resulting in more accurate and contextually relevant outputs. 3. Modular RAG: The most advanced form, Modular RAG, breaks down the process into distinct modules for retrieval, generation, and augmentation. This modularity allows for greater flexibility and optimization, leading to superior performance in complex, knowledge-intensive tasks. RAG is a game-changer for NLP, enabling more accurate, up-to-date, and contextually aware language generation. 🌟 #NLP #MachineLearning #AI #RAG #TechInnovation

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🌟 LLM Agents are truly redefining the future of intelligent automation! 🤖 The combination of LLMs with advanced modules like planning, memory, and tool usage is a breakthrough in problem-solving. These agents go beyond the capabilities of standalone LLMs by integrating external resources and employing dynamic reasoning, making them incredibly powerful. 1️⃣ Enhanced Complexity Handling: The ability of LLM agents to break down complex tasks into manageable subtasks and interface with external tools allows them to handle multi-step, intricate problems. This makes them ideal for tasks requiring real-time data, trend analysis, or even highly technical fields like drug discovery. 2️⃣ Dynamic Problem-Solving: By leveraging external resources like APIs and databases, LLM agents can go beyond static responses, addressing sophisticated queries, such as data visualization or long-term trend analysis. This adaptability allows for real-time processing and more comprehensive solutions. 3️⃣ Adaptive Learning & Memory: The incorporation of short-term and long-term memory is a game-changer. Agents can store past interactions and refine their problem-solving strategies over time, resulting in more contextually accurate and user-tailored responses. 4️⃣ Versatile Applications: From fields like drug discovery (ChemCrow) to coding assistance (ChatDev), LLM agents are transforming industries by automating complex workflows, improving efficiency, and enabling quicker decision-making. 5️⃣ Robust Frameworks: Tools like LangChain, AutoGPT, and frameworks like ReAct and Reflexion are equipping LLM agents with the structure they need to operate in dynamic, ever-changing environments, allowing them to perform a wide range of tasks with precision. LLM agents represent the next leap in AI by offering advanced capabilities for planning, adaptation, and execution. As they continue to evolve, they will be at the heart of intelligent automation, driving innovation and transforming industries across the board. Thanks for sharing this—LLM agents are definitely the future of automation and AI! #AI #LLMAgents #Automation #MachineLearning #TechInnovation #DeepLearning #FutureOfWork #LangChain #AutoGPT #AIResearch

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Have you always wondered what Machine Learning is about? Machine Learning as an integral part of career paths in Tech will be exceptionally explained and introduced this coming Sunday by a Machine Learning expert, Nasirudeen Raheem. *Title: Introduction to Machine Learning* Date: Sunday, 22nd Sept, 2024 Time: 5:30 to 6:30 PM *Register Here: http://bit.ly/4eq2hhB Don't miss this great opportunity to gain new insights to propel you further in your Tech journey.

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GPT-4o mini unveiled!!! Less than half the cost of GPT 3.5 Turbo and performance superior to GPT-4! Do share with us — What is stopping you from starting your GenAI journey?!

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🌟 Exciting News!🌟 Alhamdulillah, I am thrilled to announce the publication of our research paper titled "A Robust Ensemble Machine Learning Model with Advanced Voting Techniques for Comment Classification." In this study, we developed a cutting-edge ensemble machine learning model that leverages advanced voting techniques to significantly enhance the accuracy and reliability of comment classification tasks. Our model not only outperforms individual classifiers but also demonstrates exceptional performance on diverse and noisy datasets. Key Highlights: ✅ Innovative Ensemble Model: Combining the strengths of multiple classifiers. ✅ Advanced Voting Techniques: Improving decision-making processes. ✅ Real-World Applications:Robust against varied and noisy data. This research represents a significant milestone in the field of machine learning and natural language processing, and I am incredibly proud of the collaborative effort that made it possible. A huge thank you to my co-authors and all those who supported us throughout this journey. Here is the link: https://lnkd.in/gBwwbwuc I am excited to see the potential applications of our work and look forward to exploring new research opportunities in this area. #MachineLearning #NaturalLanguageProcessing #EnsembleLearning #CommentClassification #Research #AI #DataScience

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