Ahmed Oraby’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

RIP long study hours AI can help you learn easily in minutes That too while using all AI models like GPT-4o, Claude, and Llama, all in one place for FREE Here’s how: Save this post for later --- Go to you.com . It is the best AI-powered search engine on the internet. --- What do you get on You? 1. Get detailed information on any topic 2. Create AI art 3. Do in-depth research on any topic 4. Solve problems 5. Use any LLM you want to --- How you can use it for learning? Here is an important use case: Try this prompt in Smart Mode: Understanding Complex Concepts You are an expert professor. I am [mention the problem you’re facing in detail with context]. You are and Explain the concept of [complex topic] in simple terms. Include real-world analogies, practical examples, and a step-by-step breakdown of the core principles. Assume the reader has no prior knowledge of the subject. Additionally, provide a list of resources for further reading and a few questions to test comprehension. I want you to [mention how you want the output in detail with examples]. --- Try using You here: you.com #ArtificialIntelligence #MachineLearning #DeepLearning #AI #AIResearch #AIEthics #AITechnology #AIInnovation #AIForGood #BigData #DataScience #NLP #ComputerVision #AITrends

Understanding RAGEval: Evaluating AI’s Knowledge in Different Scenarios Imagine you have an AI system that helps answer questions by pulling information from various sources, like documents or databases This type of system is known as Retrieval-Augmented Generation (RAG). While RAG systems are powerful, evaluating how well they perform in specific situations (like finance, healthcare, or law) is tricky That’s where RAGEval comes in. This new framework automatically creates test cases for RAG systems tailored to specific scenarios. Here’s a simple way to think about it: Setting the Scene: RAGEval starts with a small set of example documents to understand the key details like names, dates, and events that are important for that specific field. Generating Content: Based on these details, it generates a variety of new documents that stick to the facts while adding some variation to test how well the AI can handle different but related information. Question-Answer Tests: RAGEval then creates a set of questions and corresponding answers from these documents. The AI’s job is to find the right answers by pulling from the generated content. Evaluating Performance: Finally, it checks how well the AI did, focusing on whether the answers were complete, free of errors (hallucinations), and relevant. #AI #MachineLearning #ArtificialIntelligence #RAG #AIEvaluation #DataScience #TechInnovation #NaturalLanguageProcessing #AIResearch #DeepLearning #NLP #AIModels #TechTrends

🚀 Why Deep Learning-Based NLP Outperform Traditional Machine Learning🚀 🔍 Key Advantages: 1️⃣ Rich Feature Extraction: Deep learning models like BERT and GPT leverage contextual embeddings, capturing nuanced meanings and relationships better than manual feature engineering. 2️⃣ Scalability: Deep learning models excel with large datasets, improving performance as more data is available, unlike traditional ML models which plateau. 3️⃣ End-to-End Learning: Deep learning integrates the entire pipeline from raw text to output, reducing the need for extensive preprocessing and feature selection. 4️⃣ Handling Complexity: Capable of modeling complex patterns and hierarchies in language, DL approaches handle syntax, semantics, and polysemy more effectively. 5️⃣ State-of-the-Art Performance: DL models consistently achieve superior results in benchmarks and competitions, pushing the boundaries of what’s possible in NLP. #DeepLearning #NLP #AI #MachineLearning #TechInnovation #DataScience #ProfessionalGrowth

🚀 #Day84-85 of #100DaysOfMLChallenge 🚀 Over the last couple of days, I’ve been deeply focused on understanding 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬, one of the most revolutionary architectures in deep learning, particularly for NLP tasks. I’ve been exploring everything from the basic structure of Transformers to the more intricate details like self-attention mechanisms and multi-head attention. These concepts are crucial for understanding how Transformers can process information more efficiently than previous models like RNNs and CNNs. Self-attention, for example, allows the model to weigh the importance of different words in a sentence relative to each other, enabling it to capture context more effectively. Multi-head attention extends this by allowing the model to focus on different parts of the input simultaneously, which significantly enhances its ability to learn complex patterns. Feel free to share your insights or resources on Transformers in the comments! Let’s keep learning together! 𝐏.𝐒 : The reason I’ve been posting two-day updates lately is to allow myself more time to thoroughly understand these advanced topics. It’s not just about covering material quickly but ensuring that I can apply these concepts practically. Taking a little extra time ensures I can bring depth and clarity to my learning journey. #100DaysOfML #Transformers #DeepLearning #AI #MachineLearning #NLP

*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

Excited to share my latest project on NLP financial sentiment analysis! 🚀 Using deep learning with LSTM, along with NLTK, tokenizer, Beautiful Soup, stop words removal, stemming, t-SNE, and word embedding (including Doc2Vec), I analyzed and predicted sentiment from financial news articles. 📊 The project involved data cleanup, preprocessing, word embedding, and model training to predict sentiment. I experimented with various models and ultimately chose LSTM due to its higher performance and ability to solve the vanishing gradient problem, which increased the accuracy of the model. Passionate about leveraging AI to solve complex problems. Let's connect and discuss to improve together! #nlp #machinelearning #ai #deeplearning #improvement #datascience #kaggle #sentimentanalysis KATBOTZ LLC Ashish Katyayan https://lnkd.in/e2EBhDG8

"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

🚀 Today’s Focus: One-Shot and Few-Shot Prompting In one-shot prompting, the model is given one example to guide its response. For instance: “ Classify the sentiment: ‘I loved the movie!’ Sentiment: Positive Classify the sentiment: ‘I didn’t enjoy the movie.’ Sentiment : ” Few-shot prompting offers several examples to improve the model’s accuracy. Larger models can handle tasks with fewer examples, while smaller models may struggle without more guidance. Tomorrow, I’ll explore model parameters and their role in LLMs’ performance! 🧠✨ #GPT #AI #GenerativeAI #LLMs #DeepLearning #Transformers #DataScience #Innovation #AIResearch #MachineLearning #NLP #Growth #InteractiveLearning #TechTrends #DigitalTransformation #LifelongLearning #ProfessionalDevelopment #LearningTogether #Knowledge #Linkedin

"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