Mahdieh Farahani’s Post

Is Writing Prompts for LLMs a Waste of Time? 🤔💡 The answer is both simple and complex: ✅ Prompts can guide LLMs to deliver more accurate results. But not all prompts are created equal, and relying on inefficient ones wastes time and resources. ⏳ 🔍 What’s the solution? A more strategic approach is needed to optimize AI training and enhance performance. At Summa Linguae, we’ve developed a 🔧 5-step LLM prompt tune-up process that’s designed to: 📊 Make AI training more data-driven and targeted to real-world applications 🌍. 🛠️ Address key challenges like prompt ambiguity and scalability, ensuring stronger and more precise models. 🤖💪 🎯 Maximize efficiency in training LLMs so that every prompt counts. 📘 Want the full breakdown of our 5-step process? Head over to the blog for all the details! Link: https://lnkd.in/grZNknpK #AI #LLMs #ArtificialIntelligence #SummaLinguae #AITraining #DataScience #PromptOptimization

Training vs. Testing Data in Machine Learning 🤖 In machine learning, one of the most crucial steps is splitting your dataset into training and testing sets. This helps ensure that your model can generalize well to new data. But why is this so important? - Training Set: This is the data your model learns from. It’s used to fit the model and adjust its parameters to make accurate predictions.    - Testing Set: This data is kept separate and only used to evaluate the model’s performance. It helps check if the model is overfitting (performing well on training data but poorly on unseen data). How do you typically split your data for machine learning projects? Image source: Internet. #MachineLearning #DataScience #AI #TrainingData #TestingData #MLTips #BigData #AIExplained #TechJourney

🚀 Day 31: Adding Data & Transfer Learning Today, I learned about techniques such as Data Augmentation, which involves modifying existing training examples to create new ones, and Data Synthesis, which utilizes artificial data input to generate new training examples. Additionally, I gained insight into Transfer Learning, which is employed in applications where data is limited. It involves leveraging pre-trained parameters from large datasets and fine-tuning them with our own data.  #AI #DataScience #machinelearning #continouslearning

𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐮𝐦𝐦𝐚𝐫𝐲: 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Have looked into this a bit lately, and there are a lot of techniques that can improve how language models perform for different tasks. There are far more advanced techniques but I just thought to share some of the ones I’ve learnt about recently: you might be using these already! Role-based Prompting: Tailoring your prompts based on the role or task you want the AI to perform. Prompt Chaining: Build a sequence of prompts that guide AI through a series of logical steps, refining its understanding at each stage. Chain of Thought: Encouraging AI to ‘think aloud’, breaking down its reasoning into steps we can follow. Zero-shot Learning: You ask AI to perform a task without prior examples, relying on its built-in knowledge. Few-shot Learning: You provide a handful of examples (around 2-5) within your prompt to steer AI in the right direction. This can be combined with the first 3 techniques to further increase the accuracy of results. #generativeai #promptengineering #learning

Thousands of AI apps out there, but you need to master 3 types; GPT as Personal Assistant, Image Generation, and Pipeline Customizer. On 25th Aug, I will explain the objectives, the syllabus, and a brief intro on the BACKWARDS AI learning framework. We will dive into how you can be 0 to 1 by effectively learning and applying AI to your professional task. I will introduce BACKWARDS: an AI Learning Framework for non-tech professionals. In the same session, we will hands-on to master any AI tools by understanding the principle of how those tools work: 1. Hands-on DrawThings/ComfyUI: Image gen with LoRa. 2. Hands-on Perplexity: Research and Certification Exercise. 3. Hands-on VectorShift: Build a custom LLM pipeline with no code. Register here: https://lu.ma/058kf6ae [Note: This is the introduction for the 7th September Workshop] #ai #workshop #aiworkshop #perplexity #vectorshift

🚀 Exciting News! 🚀 I'm thrilled to share that I've successfully completed the eXplainable AI Certificate Course on Alison.com! 🎓✨ This course has deepened my understanding of AI transparency and interpretability, equipping me with the skills to enhance AI model explainability in various applications. I'm now more prepared than ever to tackle complex AI challenges and ensure that AI systems are transparent, accountable, and trustworthy. 💡🔍 A big thanks to the Alison team for this incredible learning experience! 🙌 #ExplainableAI #AI #MachineLearning #ProfessionalDevelopment #LifelongLearning🚀 Exciting News! 🚀 I'm thrilled to share that I've successfully completed the eXplainable AI Certificate Course on Alison.com! 🎓✨ This course has deepened my understanding of AI transparency and interpretability, equipping me with the skills to enhance AI model explainability in various applications. I'm now more prepared than ever to tackle complex AI challenges and ensure that AI systems are transparent, accountable, and trustworthy. 💡🔍 A big thanks to the Alison team for this incredible learning experience! 🙌 #ExplainableAI #AI #MachineLearning #ProfessionalDevelopment #LifelongLearning

Come join us as we kick off our first PMI SCC Technical Session for 2024 with Matthew Tomlinson PMP, PgMP. Matt, a former PMI Board member, started researching Artificial Intelligence in 2015 and presented his groundbreaking work at the 2017 Gartner Symposium. He has given over 30 talks about AI and Innovation over the last decade! You don't want to miss this!

Fine-Tuning Key stages and outcomes of this process 1. Pre-Trained Model - The foundation of fine-tuning is a model that has already been trained on a vast corpus of general text data. 2. Specialized Dataset Preparation - The specific task or domain dictates the dataset for fine-tuning. 3. Fine-Tuning Process - The model undergoes additional training on this specialized dataset. This stage involves adjusting the model's weights and parameters to align more closely with the domain-specific language, style, and content. The learning rate during fine-tuning is typically lower, allowing for subtle yet effective modifications 4.Gaining Task-Specific Abilities - Post fine-tuning, the model becomes more proficient in handling the particularities of the new domain 5. Enhanced Performance in Specialized Tasks - The fine-tuned model now exhibits improved performance and accuracy in tasks related to its specialized training. 6.Customization for Organizational Needs - Organizations can leverage fine-tuning to tailor LLMs to their unique requirements. #finetuning #llm #ai

Hey Connections🤗... (I've to Repost this post because of some text Editing issues.) I want share with you all that Recently I had attended amazing workshop of AI tools which is conducted From GrowthSchool by such amazing mentor and Founder of GrowthSchool who's Vaibhav Sisinty ↗️ I realized that I utilized my time at right place in case of learning about AI tools ,it's having powerful and time savings uses. You can imagine what I've got to learn by going through the following image. If you guys want to attend same workshop for free then Here’s their platform on which you can attend this workshop of AI tools for free of cost :- https://lnkd.in/gdfWp9Su #ai #aitools2024 #newera #artificialintelligence #prompting #promptEngineering #reposted #reposting #gained #Upgrade ##aitools

If AI intimidates you - Learn about it! I recently completed the Harvard Business School Online AI for Business course, and it was an eye-opening experience. Not only did it provide me with the foundational knowledge to leverage AI effectively, but it also sparked my creativity with numerous potential use-cases. After completing the course, I can confidently say that I am no longer intimidated by AI and am now actively incorporating AI into my daily work routines. And yes, I chose to rewrite this with AI 😉 😂 #AI #HarvardBusinessSchool #ArtificialIntelligence #Learning #ProfessionalDevelopment