Vaibhav Narute’s Post

This description introduces and summarizes the concept of RAG (Retrieval-Augmented Generation) enhancements as shown in the image. Let me break it down further: 1. RAG : RAG is a technique that combines information retrieval with text generation. It allows AI models to access and use external knowledge when generating responses, improving accuracy and reducing hallucinations. 2. Frontier of enhancements: The image showcases various advanced techniques and methods being developed to improve RAG systems. These represent the cutting edge or "frontier" of research and development in this field. 3. Input optimization to adaptive retrieval: This phrase highlights the breadth of enhancements, from improving how queries are processed (input) to sophisticated methods for retrieving relevant information (adaptive retrieval). 4. Shaping the future: These techniques are expected to significantly impact how AI systems access, process, and generate information in the future. 5. AI-powered information access and generation: This emphasizes the dual nature of RAG - it's not just about retrieving information, but also using that information to generate more accurate and contextually relevant responses. The description aims to convey the importance and potential impact of these RAG enhancements in the field of AI, making it suitable for a professional audience on LinkedIn who might be interested in or working on advanced AI technologies. #Ai #GenAi #Python #RAG #LLM #Langchain #PromptEngineering #Chunk #Vectordb

🤯 We need to talk about DeepSeek! DeepSeek, a Chinese company, released a new model called DeepSeek-R1 on January 20, 2025. This is a huge turning point in AI. Why? 1️⃣ Open Source: DeepSeek-R1 is fully open-source, allowing researchers and developers to study, modify, and build upon the model for FREE. 2️⃣ Performance: It outperforms OpenAI's o1 and Claude 3.5 Sonnet on several key benchmarks, particularly in mathematics, coding, and reasoning tasks. 3️⃣ Training Costs: DeepSeek-R1 was developed for just $5M compared to the billions of dollars spent by OpenAI, Meta, Anthropic, etc. 4️⃣ Pricing: It costs just $2.19 for a million output tokens, compared to $60 for o1, making advanced AI capabilities much more accessible. 5️⃣ Innovative Architecture: DeepSeek-R1 uses a MoE approach with 671B parameters and employs a groundbreaking reinforcement learning (RL)-centric approach. The model also has sparked a fierce debate on Chinese vs American innovation, with some baselessly accusing DeepSeek of being a copycat. All this while, India is sleeping. We need to wake up and do something about it. —— I can't contain my excitement about DeepSeek. We are organizing a 1.5-hour session on DeepSeek - where I'll tell you everything about the model and build some cool apps and agents. Register now: https://lu.ma/ael5tq70 #AI #DeepSeek #Innovation #BuildFastwithAI #FreeWebinar

✅I agree with you Imagine an ai losing its job because of another ai competitor😂. although some of the rumors caught that This model has also reignited debates on Chinese vs. American AI innovation, with some claiming it’s just another copy. But in reality, it challenges the status quo of AI development. As a backend developer, I see this as a huge opportunity—whether for AI-powered applications, cost-effective AI solutions, or deeper learning in LLM architectures. What do you think? Is DeepSeek AI -R1 the future of AI, or just another competitor in the race? #AI #DeepSeekR1 #opensourceai #ArtificialIntelligence #TechInnovation #BackendDevelopment

AI vs Machine Learning: Unpacking the Differences 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dnGPWSJR Artificial Intelligence (AI) and Machine Learning (ML) are two interconnected yet distinct concepts that often get conflated. AI refers to the broader field of research focused on creating intelligent machines that can perform tasks that typically require human intelligence, such as reasoning, problem-solving, and learning. Machine Learning, on the other hand, is a subset of AI that specifically deals with developing algorithms and statistical models that enable machines to learn from data, without being explicitly programmed. The key differences between AI and ML lie in their scope, goals, and approaches. While AI aims to create systems that can think and act like humans, ML focuses on enabling machines to learn from data and improve their performance over time. To deepen your understanding of AI and ML, consider exploring the following areas: - Study the history of AI and ML to appreciate the evolution of these fields - Learn the fundamentals of programming languages like Python, R, or Julia to gain hands-on experience with ML algorithms - Delve into the theoretical foundations of AI and ML by reading research papers and articles Additional Resources: None #AIvsMachineLearning #STEM #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #DataScience #ComputerScience #Technology #Innovation #FutureOfWork Find this and all other slideshows for free on our website: https://lnkd.in/dnGPWSJR #AIvsMachineLearning #STEM #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #DataScience #ComputerScience #Technology #Innovation #FutureOfWork https://lnkd.in/dWH2cBEF

🌟 Day 2: Embeddings & Vector Databases Yesterday, I embarked on an incredible deep dive into the world of embeddings and vector databases—the unsung heroes behind many Large Language Model (LLM) capabilities. From understanding the conceptual foundations of embeddings to exploring their real-world applications, it’s been an eye-opening journey. The trade-offs in designing these systems? Fascinating and essential for crafting efficient, scalable solutions! 🔍 Hands-On Highlights: Here’s what I explored in today’s code lab: ✳️ RAG-Based Question-Answering System I implemented a Retrieval-Augmented Generation (RAG) model to answer questions from a custom document set. It was amazing to see how embeddings enhanced the precision of document retrieval, enabling dynamic, contextually rich responses! ✨ Text Similarity Analysis Using various embedding techniques, I compared text inputs for similarity. The nuanced differences in embeddings highlighted the model’s ability to grasp subtle connections between seemingly different pieces of text—truly mind-blowing! 🛠️ Neural Classification with Keras Leveraging embeddings in Keras, I built a basic neural classification network. The embeddings added remarkable precision to text classification tasks, underscoring their importance in complex language modeling. 📊 Key Insights Today’s work reinforced how embeddings elevate LLMs by enabling precise information retrieval, text classification, and dynamic data connections. Combined with vector databases, they unlock endless possibilities for custom AI applications—definitely a game-changer! And of course, daily live streams with experts like Logan Kilpatrick, Lee Boonstra, Aliaksei Severyn, Majd Al Merey, Mohammadamin Barekatain, Daniel J. Mankowitz, Chuck Sugnet, and Abdellahi El Moustapha. continue to deepen our understanding of this transformative tech. Can’t wait to uncover more insights tomorrow—stay tuned! #GenAI #LLM #Embeddings #VectorDatabases #ArtificialIntelligence #RAG #MachineLearning #DataScience #Innovation Kaggle Google

Excited to share my first project on finetuning a Large Language Model (LLM)! 🌟 Coming from a People Management domain, understanding employee sentiment and moderating content is crucial. I used to spend hours analyzing survey responses and assessing stakeholders' emotional states. This motivated me to create a model that detects toxic content in large datasets instantly, aiding informed decision-making in the people function. The goal was to fine-tuned the BERT-based LLM for identifying the toxicity of tweets, improving its performance and making it more efficient in detecting cyberbullying. This involved extensive text preprocessing, model training, and evaluation on PyTorch. One of the biggest challenges was managing the computational demands of fine-tuning a large language model that includes GPU usage. However, I switched between my Colab accounts to strategically utilize the GPU limit. I'm proud to say that the finetuned model now performs significantly better with an improved accuracy and f1 score of 0.99 each. This project has been a fantastic learning experience! I'm looking forward to applying these skills in future projects and continuing to explore the vast possibilities of AI. Feel free to connect if you're interested in discussing AI, machine learning, or any exciting projects! Check out the project on GitHub: [https://lnkd.in/dmqGHN-q] Explore the model on Hugging Face: [https://lnkd.in/dPyimhCz] #AI #MachineLearning #LLM #Finetuning #DataScience #LearningJourney

🚀 Unlocking the Power of Pre-Trained Models: Weekend Project! 🌟 Imagine harnessing the power of AI to sift through the latest news in Artificial Intelligence (AI) and Machine Learning (ML)—and presenting it in an insightful, easy-to-understand format! 📰💡 That’s exactly what I’ve created with my new AI-Powered News Analyzer! Here’s why you should check it out: 🔍 Discover Hidden Insights: Using a pre-trained model, this tool dives deep into trending news articles, providing analysis that highlights key trends and implications in the AI and ML space. ⏳ Save Time & Effort: Gone are the days of combing through endless articles. This project simplifies your information gathering, making it easier than ever to stay informed about the latest developments in technology. 💡 Experience Cutting-Edge AI: By leveraging existing models, I’ve tapped into state-of-the-art capabilities that anyone can use! No PhD required—just a curiosity for what AI can do. 🚀 Join the AI Revolution: Whether you’re a tech enthusiast, a data scientist, or simply someone interested in AI, this tool is designed to empower you. 🌐 Check it out now! You won't want to miss this chance to see how AI is reshaping the future of information. App Link : https://lnkd.in/gQARZafN Let’s explore the limitless potential of AI together! 🤖✨ #AIRevolution #MachineLearning #DataScience #PreTrainedModels #Innovation #WeekendProject #StayInformed #LLM #GPT2 #Huggingface #gradio #python #news

𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗨𝗽𝗱𝗮𝘁𝗲: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗧𝗼𝘅𝗶𝗰𝗶𝘁𝘆 𝗠𝗼𝗱𝗲𝗹 𝘄𝗶𝘁𝗵 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄🚀 I’m thrilled to share a recent development where I created a comment toxicity detection model using TensorFlow and Gradio! 🎉🤖 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: 📊 Loaded and preprocessed a dataset of comments, featuring labels like toxic, severe_toxic, obscene, threat, insult, and identity_hate. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: 🏗️ Built a powerful sequential model with an embedding layer, bidirectional LSTM, and dense layers to capture the nuances of text data. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: 📈 Trained and evaluated the model, achieving impressive results with a precision of 84.8% and a recall of 63.4%. 🥳 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗲𝗺𝗼: 💻 Deployed an interactive demo using Gradio to easily test the model’s predictions on new comments. 𝗞𝗲𝘆 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗨𝘀𝗲𝗱: TensorFlow 📚 Gradio 💬 TextVectorization 🔤 LSTM 🧠 𝗚𝗶𝘁𝗵𝘂𝗯 𝗹𝗶𝗻𝗸: https://lnkd.in/gxZjpuAs 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀: 🔍 Improving the model’s performance and fine-tuning hyperparameters. ✨ Exploring additional features to enhance toxicity detection. #MachineLearning #DeepLearning #TensorFlow #NaturalLanguageProcessing #DataScience #AI #Gradio #TextClassification #CommentToxicity #TechProjects #MachineLearningProjects #DeepLearningProjects

🚀  𝐏𝐢 𝐀𝐈 𝐖𝐞𝐞𝐤𝐥𝐲 𝐓𝐫𝐞𝐧𝐝𝐬 #𝟐𝟐 𝐢𝐬 𝐡𝐞𝐫𝐞! It’s Friday! Get ready to stay ahead with the latest AI breakthroughs, handpicked by our Senior Deep Learning Scientist, Àlex R. Atrio. This week’s highlights: 📚𝐃𝐢𝐟𝐟𝐋𝐌: a framework for synthetic data generation with LLMs. It achieves great results on several benchmarks for structured generation by combining a Variational Autoencoder trained on a dataset and a Diffusion Model LM to generate structured output based on the original data. 🌐 https://pischool.link/h5t 🐦𝐌𝐀𝐆𝐏𝐈𝐄: a promising method to create high-quality synthetic instruction data by extracting it from instructed open-weight models like Llama-3-8B-Instruct, which don’t open-source their alignment data. Synthetic user queries are generated by inputting only the pre-query templates up to the position reserved for the user message to the instructed LLM. 🌐 https://pischool.link/7ia 🤖𝐀𝐝𝐚𝐩𝐭𝐢𝐧𝐠 𝐖𝐡𝐢𝐥𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: this method targets the complex issue of solving scientific problems with LLMs by connecting the model with external tools, such as APIs, numerical solvers, or a Python code interpreter. They train an LLM to subdivide a given complex scientific problem, label the resulting sub-problems based on their difficulty, and then choose the most suitable tool for each sub-problem. 🌐 https://pischool.link/0ga Was this helpful? Let us know by liking and sharing! #AI #MachineLearning #DeepLearning #FoundationModels #PiAIWeeklyTrends

Day 2: Embeddings, Vector Databases, and Text Similarity 🧠🔍 Today's Highlights: 1.) Understanding Embeddings: Dove deeper into the conceptual foundations of embeddings and how they represent text as numerical vectors. 2.) Vector Databases and Retrieval: Explored the role of vector stores and databases in bringing live or specialist data into LLM applications. 3.) Text Similarity Workflows: learned techniques to leverage embeddings for classifying and comparing textual data. Hands-On Practice: >> Built a RAG (Retrieval-Augmented Generation) question-answering system over custom documents >> Experimented with text similarity using embeddings on Kaggle >> Constructed a neural classification network with Keras, utilizing embedded Key Learnings: 1.) Embeddings Power: Understand how embeddings capture semantic relationships between words and documents, enabling powerful text understanding. 2.) Vector Databases: learned how these specialized databases allow for efficient storage and retrieval of embedding-based data. 3.) Text Similarity Workflows: Explored techniques to measure and apply text similarity, from classification to recommendation systems. Essential Concepts: >> Embedding Algorithms (e.g., Word2Vec, GloVe, BERT) >> Approximate Nearest Neighbor (ANN) Search >> Cosine Similarity, Euclidean Distance >> Transfer Learning with Pretrained Embeddings Prompting Techniques: >> Retrieval-Augmented Generation (RAG) >> Embedding-based Information Retrieval Looking Ahead to Day 3: Tomorrow, we'll dive into the intriguing world of multimodal AI, exploring how we can leverage both text and visual data to unlock new capabilities. Can't wait to see what insights and hands-on skills await! Let me know if you have any other questions about today's learnings or the 5-Day Gen AI Intensive. I'm happy to discuss further! #GenerativeAI, #AITraining, #MachineLearning, #PromptEngineering, #AIInnovation, #LLM, #DataScience, #Kaggle, #AICommunity, #TechLearning, #Embeddings, #VectorDatabases, #TextSimilarity