ZAVI on LinkedIn: #ai #education #innovation #zavi #studylabs (2024)

Google is now giving out AI Courses for free These 8 AI courses makes you an AI expert within days:1. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜This introductory-level microlearning course explains what Generative AI is, how it is used, and how it differs from traditional machine-learning methods.🔗https://drp.li/lXXa92. 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀This is an introductory-level microlearning course that explores what large language models (LLMs) are and the use cases where they can be utilized.🔗https://drp.li/qj99G3. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜This course aims to explain what responsible AI is, why it's important, and how Google implements responsible AI in their products.🔗https://drp.li/BrAJP4. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗻 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.🔗https://drp.li/uNoGR5. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲Learn about the main components of the encoder-decoder architecture and how to train and serve these models.🔗https://drp.li/GdaUr6. 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence.🔗https://drp.li/lxug77. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗕𝗘𝗥𝗧 𝗠𝗼𝗱𝗲𝗹This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.🔗https://drp.li/cU86t8. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗜𝗺𝗮𝗴𝗲 𝗖𝗮𝗽𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀By the end of this course, you will be able to create your image captioning models and use them to generate captions for images.🔗https://drp.li/tZftuLike & Repost to help othersFollow AMMAR AHMED for more

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🚀 Exciting Developments in Generative AI: Transfer Learning Unveiled! 🤖As the field of artificial intelligence continues to evolve, one of the most promising areas of innovation is Generative AI. Within this realm, Transfer Learning stands out as a game-changer, revolutionizing how we approach AI models and their applications.Transfer Learning, a technique inspired by the human brain's ability to transfer knowledge from one task to another, is gaining significant traction in the realm of Generative AI. This methodology involves leveraging pre-trained models on vast datasets and fine-tuning them for specific tasks or domains, rather than training from scratch. The result? Faster development cycles, improved performance, and enhanced efficiency.Here are some key types of Transfer Learning in Generative AI:Fine-tuning Pre-trained Models: This involves taking a pre-trained model, such as OpenAI's GPT or Google's BERT, and fine-tuning its parameters on a smaller, domain-specific dataset. By adjusting the model's weights, it can adapt to new tasks or generate content tailored to specific contexts.Feature Extraction: In this approach, the pre-trained model's early layers, which capture general features, are retained, while the later layers are replaced or modified to suit the target task. This enables the model to extract relevant features from the input data, enhancing its generative capabilities.Domain Adaptation: Sometimes, the target domain may differ significantly from the domain on which the pre-trained model was trained. Domain adaptation techniques aim to bridge this gap by aligning the feature distributions between the source and target domains, thereby improving the model's performance in the target domain.Multi-task Learning: Rather than fine-tuning a pre-trained model for a single task, multi-task learning involves training the model simultaneously on multiple related tasks. This enables the model to leverage shared knowledge across tasks, leading to improved performance and generalization.In conclusion, Transfer Learning is a powerful tool in the arsenal of Generative AI researchers and practitioners, unlocking new possibilities for rapid prototyping, domain adaptation, and knowledge transfer. Embracing these techniques can accelerate innovation and drive breakthroughs across various domains. Let's continue pushing the boundaries of Generative AI together! 🔍🌟 #GenerativeAI #TransferLearning #ArtificialIntelligence #Innovation #AIResearch

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I’m excited to share my latest blog on Medium aboutMatryoshka Representation Learning (MRL)! This innovative approach enhances how we represent data in machine learning, enabling models to adapt to various tasks with different computational needs.In the blog, I explore:👉What is MRL?: A technique that allows flexible embedding sizes, optimizing the representation of data without additional computational costs.👉Training and loss function : How to train embedding models which can flexible to differnt embedding dimentions with loss of generality and accuracy for downstream tasks.👉Results and Applications :How MRL can significantly improve efficiency in tasks like image classification and multimodal retrieval.Check out the blog for a deeper dive into how MRL can revolutionize representation learning in AI! https://lnkd.in/gN7jtZSq#MachineLearning #AI #RepresentationLearning #DataScience #MRL

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How Does AI Actually Work?Have you ever been amazed by how good AI recommendation systems are at surfacing the perfect YouTube videos, Netflix shows or product suggestions based on your interests? The powerful AI models behind these systems employ machine learning on a massive scale to accurately predict what each individual user will most likely want to consume next.Recommendation engines leverage both collaborative filtering techniques that analyze patterns across many users, as well as content-based filtering using machine learning to understand the attributes and metadata of each piece of content. By combining these approaches, AI can make highly personalized and relevant recommendations. But how does this process actually work?[Step-by-Step Guide]1) Data CollectionFor each user, data on browsing activity, searches, purchases etc is gatheredContent metadata is also collected - descriptions, genres, categories, etc2) Data PreprocessingUser data is processed to extract features like preferences, behaviorContent data is transformed into representative feature vectors3) Model SelectionCollaborative Filtering uses matrix factorization, neighbor-based techniquesContent-based models use machine learning like deep neural networks4) Model TrainingOn user-item interaction data, models learn patterns of similar users/itemsOn content data, models learn to map features to what users engage with5) Model EvaluationTechniques like holdout testing measure how good recommendations areMetrics like Precision, Recall, NDCG quantify accuracy6) Model TuningHyperparameters are tuned, ensemble methods combine multiple modelsTechniques like matrix factorization can improve recommendations7) Model DeploymentDeploying trained models as a service to production applicationsProvides recommendation scoring in real-time8) Monitoring & RetrainingRecommendation quality is monitored via A/B tests, user studiesModels are periodically retrained as new data comes inBy applying advanced machine learning to comprehensive user data and content metadata, AI recommendation systems get incredibly smart at surfacing hyper-personalized and engaging content for every individual.For any of your AI needs, from recommendation engines to computer vision and natural language processing, contact us to leverage machine learning solutions for your business.#AI #MachineLearning #RecommendationEngines #PersonalizationAI #CollaborativeFiltering #TechAI

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Let's dive into some popular generative AI models that are revolutionizing the way we interact with text! 📝💡GPT (Generative Pre-trained Transformer):Key Features: Imagine a brainy Transformer! GPT models learn from vast amounts of text data and excel at crafting coherent and contextually relevant text. From text completion to dialogue generation, they're the wizards of NLG tasks!OpenAI GPT-3:Key Features: Meet the superstar of NLG! 🌟 With a whopping 175 billion parameters, GPT-3 is a powerhouse for generating human-like text across various topics and styles. It's like having a versatile wordsmith at your fingertips!BERT (Bidirectional Encoder Representations from Transformers):Key Features: BERT isn't just about understanding; it can also spin a good yarn! 📚 By conditioning on specific prompts or contexts, BERT-based models churn out coherent and contextually rich text, even though they weren't designed for it!GPT-2 (Generative Pre-trained Transformer 2):Key Features: The predecessor to GPT-3, GPT-2 may be smaller, but it's still mighty! 💪 Whether it's completing text, crafting stories, or summarizing content, GPT-2's got the knack for generating diverse and relevant text.XLNet:Key Features: XLNet takes a unique approach with permutation-based training, capturing bidirectional context like a pro! 🔄 This results in text outputs that are not only coherent but also rich in context and style.These AI models are more than just fancy algorithms; they're reshaping how we interact with language! 🌐 From crafting content to powering conversational agents, they're at the forefront of NLG innovation, bringing us closer to seamless human-computer interaction. 🚀✨Excited to see where these advancements take us next? Let's keep the conversation going! Share your thoughts below! 👇💬 #NLG #AI #Innovation #TextGeneration

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My Key Learnings: A Brief Overview🎓 Taught by Gwendolyn Denise Stripling, Ph.D.🔍 1. Defining Generative AI Generative AI is like a digital artist, capable of creating diverse content such as text, images, audio, and synthetic data. Picture a canvas where algorithms unleash their creativity, painting endless new possibilities!🤖 2. How Does Generative AI Work? Consider Generative AI as the imagination of AI. Unlike traditional rule-based systems, it learns from data. Deep learning, a key subset of Generative AI, mirrors the structure of neural networks, weaving intricate and complex patterns.🔢 3. Different AI ModelsSupervised vs. Unsupervised: Supervised learning involves labeled data (akin to recognising a Himalayan thangka it has been shown), while unsupervised learning delves into uncovering hidden patterns within data such as an algorithm suggesting different items based on what you have selected on Amazon.Deep Learning: This involves multiple layers of neurons, surpassing traditional models in complexity. Imagine it as a symphony composed of layers of code, each contributing to the harmony of the whole system.🌐 4. Generative ApplicationsDiscriminative Models: These models classify and predict, much like a seasoned food critic who assesses and categorizes culinary delights. Here, labels are crucial.Generative Models: These models craft new data, forecasting the unpredictable. Mistakes, or "hallucinations," are merely creative errors that add to the innovation process.🚀 Why It Matters? Generative AI is a powerhouse of innovation. Imagine Figma automatically renaming layers or generating personalized content tailored to individual preferences. 🎨✨#GenerativeAI #MachineLearning #DataScience #AIInnovation #DeepLearning #DesignThinking #TechTrends #CreativeAI #PatternRecognition #Figma #UI #UX

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Today's Tips - LLM: Real-world ApplicationsDiscover the groundbreaking fusion of TensorFlow and RAG in AI, particularly in natural language processing and machine learning, showcasing their combined efficacy in real-world scenarios:🤖 **Chatbot Innovation**: See how TensorFlow and RAG drive innovation in intelligent chatbots, exemplified by XYZ Corporation. With an accuracy rate exceeding 90%, TensorFlow understands natural language queries, while RAG provides contextually fitting responses, boosting user satisfaction by 40% compared to traditional chatbots.🔧 **Customer Support Optimization**: ABC Inc.'s customer support system, integrating TensorFlow for intent recognition and RAG for response generation, achieves a 50% reduction in response time and a 30% enhancement in issue resolution rates. Witness how this blend streamlines customer interactions effectively.📚 **E-learning Personalization**: Dive into the e-learning sphere, where a pioneering platform uses TensorFlow to analyze learning patterns and RAG to deliver tailored learning materials. Experience a 25% increase in course completion rates, emphasizing the role of personalized content delivery in enhancing user engagement and success.💬 **Healthcare Chatbot Case Study**: Explore a healthcare application by HealthTech Co. leveraging TensorFlow for symptom analysis and RAG for personalized health advice. Metrics reveal a 60% improvement in diagnostic accuracy and a 70% increase in patient engagement, highlighting the transformative potential of this technology in critical domains.#AI #MachineLearning #Chatbots #CustomerSupport #Elearning #Healthcare #Technology #Innovation

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Over 97 Million Jobs Will be Created by AIGoogle just released 5 FREE Gen AI courses 🤩 1) Introduction to Generative AI- Learn what Generative AI is, its applications, and how it differs from traditional machine learning.- [Enroll Here](https://buff.ly/3WzfleE)2) Introduction to Large Language Models- Discover what large language models (LLMs) are, their use cases, and how to enhance their performance with prompt tuning.- [Enroll Here](https://buff.ly/3Yvaw7x)3) Introduction to Responsible AI- Understand what responsible AI means, its importance, and Google’s 7 AI principles.- [Enroll Here](https://buff.ly/3y2AWTb)4) Introduction to Image Generation- Get to know the theory behind diffusion models and learn to train and deploy them on Vertex AI.- [Enroll Here](https://buff.ly/3Y6kImC)5) Introduction to Generative AI Studio- Get familiar with Generative AI Studio on Vertex AI and learn how to prototype and customize AI models for your applications.- [Enroll Here](https://buff.ly/3Yia2l3)Comment below if you found this helpful.👇 #aijobs #chatgpt#typescouts

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#generativeaitools #data #booksrecommendation #bookreview #chatgpt The Complete Obsolete Guide to Generative AI: Review of Chapter 1 ⚠️ Follow for Live Updates

🌟 Dive into the World of AI! 🤖📚Navigating the vast ocean of AI resources can be daunting, with countless YouTube videos and courses available. But where do you start for a solid, comprehensive understanding?Here's a gem I highly recommend: the https://a16z.com/ai-canon/ 📚 This repository is a treasure trove of well-organized, in-depth material, perfect for anyone eager to get a comprehensive taste of AI technology. 🌐💡Check it out ! Andreessen HorowitzShare this to spread the knowledge and help others embark on their AI journey! 🌍💬#AI #MachineLearning #TechInnovation #A16z #Learning #KnowledgeSharing

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