ANKUSH BHAT’s Post

I'm thrilled to share the demo of our graduation project, SummaryFlow: AI Book Summarization! 🎓📚 Project Overview: Our project tackles the challenge of summarizing long narrative novels using state-of-the-art AI techniques. We implemented both extractive and abstractive summarization methods, fine-tuning the FLAN-T5 base model on the BookSum dataset. This allows the model to summarize small sections of a novel first and then recursively summarize these summaries to create a coherent and concise overall summary. Key Features: - Advanced Preprocessing: Converts raw PDF texts into high-quality input for the summarization model. - Recursive Summarization: Breaks down the novel into manageable sections and iteratively summarizes them. - High-Quality Output: Produces accurate and coherent summaries of entire novels. Check out the demo video to see SummaryFlow in action! I hope it demonstrates the potential of AI in transforming how we process and consume lengthy texts. Thank you to my team and mentors for their incredible support throughout this journey. Excited to share our work with the community! #AI #MachineLearning #NLP #BookSummarization #GraduationProject #AinShamsUniversity #Demo

🌟 Understanding Self-Attention vs. Multi-Head Self-Attention 🌟 In the world of deep learning, particularly with transformers, understanding the mechanisms of self-attention and multi-head self-attention is crucial for leveraging their full potential. Here’s a quick breakdown of the key differences: 🔍 Self-Attention: - Analyzes relationships between elements in a sequence. - Transforms each element into Query, Key, and Value vectors. - Computes compatibility scores to determine how much focus each element should have on others. - Creates a context-aware representation by weighting Value vectors. ✨ Multi-Head Self-Attention: - Runs multiple self-attention processes in parallel, allowing the model to focus on different aspects. - Projects input into multiple Q, K, and V vectors for each head. Calculates separate attention scores and outputs, then concatenates them for a richer representation. 💡 Analogy: - Self-attention is like understanding how each word in a sentence relates to others to grasp overall meaning. - Multi-head self-attention is akin to reading the sentence multiple times, focusing on different elements like grammar, context, and sentiment, and then combining insights for a deeper understanding. These mechanisms are foundational in tasks like machine translation, enabling models to capture long-range dependencies effectively. As we continue to innovate in deep learning, self-attention and multi-head self-attention will remain pivotal in advancing our capabilities in processing complex sequential data. #SelfAttention #MultiHeadSelfAttention #Transformers #DeepLearning #NLP #AI #MachineLearning

🚀 Hi LinkedIn, Excited to share my latest project: Text Summarizer! 📄✨ In our fast paced world, reading lengthy texts can be a challenge. To tackle this daily life problem, I developed a program that quickly and efficiently summarizes long texts. 📝💡 Watch the video to see how it works: - Input a long text. - Run the algorithm, which preprocesses, scores, and summarizes the content. - Get a concise summary in no time! This project leverages advanced techniques like feature scoring and neural network models to deliver accurate and relevant summaries. Save time and get straight to the point. ⏱️📚 #TextSummarization #NaturalLanguageProcessing #MachineLearning #AI #DataScience #TechInnovation #NLP #DeepLearning

🎓 𝗖𝗿𝗮𝗰𝗸 𝘁𝗵𝗲 𝗖𝗼𝗱𝗲 𝗼𝗳 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗧𝗼𝗼𝗹! 🤖 Curious about how Convolutional Neural Networks (CNNs) work but find them complex? You’re not alone! Fortunately, CNN Explainer (https://lnkd.in/dRyx9DEb) is here to make learning CNNs both easy and engaging. Why You’ll Love CNN Explainer: 🌟 Visual Learning: Explore how each layer of a CNN processes and transforms images, step by step, through clear visualisations. 🌟 Beginner-Friendly: No advanced math skills required—this tool is perfect for beginners in deep learning and AI. 🌟 Interactive and Hands-On: Adjust parameters, tweak layers, and see real-time effects, making abstract concepts easy to grasp. 🌟 Upload Your Own Images: The tool is pre-trained on categories like lifeboats, ladybugs, pizzas, koalas, and more. Upload your own image from these categories and watch the CNN make predictions live! 🌟 Clear Explanations: Annotated visuals break down complex CNN concepts into simple, understandable pieces. Who’s It For? Students & Educators: An ideal resource for teaching and learning AI and deep learning fundamentals. AI & Machine Learning Beginners: A great way to kickstart your journey in artificial intelligence. Data Science Enthusiasts: Enhance your understanding of CNNs with this practical tool. Experienced Professionals: Even seasoned AI practitioners will appreciate the intuitive explanations and visual learning. Ready to demystify Convolutional Neural Networks? Dive in here: https://lnkd.in/dRyx9DEb #AI #DeepLearning #MachineLearning #DataScience #ConvolutionalNeuralNetworks #CNN #LearningResources #ArtificialIntelligence #NLP #AIForBeginners

Hello everyone, I wanted to share some insights about our #AI research at Antematter R&D Lab. In our second research study, we are working on fine-tuning a Large Language Model (#LLM) to integrate new domain-specific knowledge. In the upcoming posts, we'll explore this exciting research in detail 🚀 If you've experience with fine-tuning LLMs, I would greatly appreciate your valuable suggestions. Let's keep the conversation going by sharing your thoughts and insights 🤝 #AI #Research #GenerativeAI #RnD #AIresearch #NLP #LLM #Finetuning #AntematterRnD #Casestudy #Technology #Innovation

*Day 1* of the "Learn with GUVI" event was a deep dive into the world of AI! We explored the fundamentals of *Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, and NLP*. The session was packed with insights on how these technologies work together to create intelligent systems. A special highlight was getting hands-on with GPT, learning how it generates human-like text. Big thanks to Dhana Vasanth for making these complex concepts so clear and engaging. Excited to see what innovative AI solutions we'll build this week! Excited to explore GUVI's "https://dreamer.guvi.in/" a cutting-edge AI image generator that's changing how we create and customize visuals. Here's a sneak peek of what I've been working on! #Guvi #LearnWithGuvi #AI #MachineLearning #DeepLearning #ComputerVision #NLP #GPT #BuildYourOwnApp #AI #ImageGeneration #Guvi #LearnWithGuvi #Dreamer

"Transforming Data with Feature Engineering: A Key to Model Success" 🔍 Feature Engineering: The Secret Sauce to Enhancing Your Models! Feature engineering is crucial for improving model performance. Here’s how you can leverage it: Create New Features: Derive features from existing ones (e.g., creating "age" from "birthdate"). Feature Selection: Choose the most relevant features to reduce noise (e.g., using correlation matrices or feature importance scores). Normalization & Scaling: Ensure your features are on a similar scale (e.g., Min-Max Scaling or Standardization). 💡 Quick Tip: Experiment with different feature transformations and selections to see what works best for your model. #AI #ML #DeepLearning #NLP #GenerativeAI #DataScience #MachineLearning #FeatureEngineering

"Boosting Model Performance: A Quick Dive into Hyperparameter Tuning" 🚀 Tip for AI/ML Engineers: Maximize Model Performance with Hyperparameter Tuning! Hyperparameter tuning can make or break your model's performance. Here’s a quick guide to get you started: Understand Hyperparameters: These are not learned from the data but set before training (e.g., learning rate, tree depth, etc.). Popular Techniques: Grid Search: Exhaustive search over specified parameter values. Random Search: Random combinations of parameters for faster results. Bayesian Optimization: Smarter search by balancing exploration and exploitation. 🎯 Pro Tip: Start with Random Search for quicker insights and then fine-tune with Grid Search. #AI #ML #DeepLearning #NLP #GenerativeAI #DataScience #MachineLearning #TechTips

I’m thrilled to announce that I have successfully completed the “Introduction to Prompt Engineering” certification! 🚀 This course has equipped me with valuable skills in crafting effective prompts for AI systems, enabling me to drive better outcomes in natural language processing, conversational AI, and other machine learning applications. From understanding the nuances of AI communication to mastering prompt optimization, this certification has provided deep insights into how we can make AI systems smarter and more responsive. I’m excited to apply these skills in future projects, leveraging prompt engineering to unlock the full potential of AI in real-world use cases. #PromptEngineering #AI #MachineLearning #ArtificialIntelligence #NLP #CareerGrowth #ContinuousLearning

*Unlock the Power of Self-Attention in Transformers! 🔓* I'm thrilled to share my latest blog post, where I delve into the fascinating world of self-attention mechanisms in transformers! 🔍✨ Discover how self-attention works its magic, why it's a game-changer for modern NLP models, and how it drives state-of-the-art architectures. Read now and elevate your AI expertise! 🚀 *Check it out here:* ( https://lnkd.in/gkQ_PqXM ) #AI #MachineLearning #DeepLearning #Transformers #SelfAttention #NLP #ArtificialIntelligence #DataScience