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"A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks" by Shubham Vatsal and Harsh Dubey: 📝 New Research Alert: Prompt Engineering in Large Language Models 📝Exciting new paper by Shubham Vatsal and Harsh Dubey! 🌟 🔍 Title: A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks 📅 Submitted: July 17, 2024 🔹 Overview: This paper provides a comprehensive survey of prompt engineering techniques used in Natural Language Processing (NLP) tasks with Large Language Models (LLMs). Prompt engineering involves composing natural language instructions (prompts) to elicit knowledge from LLMs in a structured way, without the need for extensive parameter re-training or fine-tuning. 🔹 Key Insights: Performance Gains: Prompt engineering has shown significant performance gains on various NLP tasks by leveraging the embedded knowledge of LLMs. Accessibility: The approach democratizes access to advanced NLP techniques, allowing even those without a deep mathematical machine learning background to experiment with LLMs through basic natural language conversational exchanges. Techniques and Taxonomy: The authors summarize different prompting techniques and categorize them based on the NLP tasks they are used for. They present a taxonomy diagram and discuss the state-of-the-art (SoTA) methods for specific datasets. Research Survey: The paper reviews 44 research papers discussing 39 different prompting methods applied to 29 different NLP tasks, most of which were published in the last two years. 📚 Link to Paper: Read the full paper here 👉 Why It Matters: This research highlights the importance of prompt engineering in enhancing the capabilities of LLMs without the need for extensive re-training. It democratizes access to advanced NLP techniques, making it easier for a broader audience to engage with and benefit from LLMs. #AI #MachineLearning #NLP #Research #PromptEngineering #DataScarcity

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🚨ACL 2024 Outstanding Paper Award Alert 🚨 ➡️Paper Title: INDICLLMSUITE: A BLUEPRINT FOR CREATING PRE-TRAINING AND FINE-TUNING DATASETS FOR INDIAN LANGUAGES 🌟Few pointers from the paper 🎯Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. 👁In this paper Authors work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. 🎯Recognizing the importance of both data quality and quantity, their approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. 🎯They built a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. 🎯For instruction-fine tuning, they amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. 🎯Additionally, they addressed toxicity alignment by generating toxic prompts for multiple scenarios and then generated non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. 🎯 They hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. 🏢Organization: 🛎️Nilekani Centre at AI4Bhārat🛎️ Indian Institute of Technology, Madras 🛎️Microsoft🛎️Sant Longowal Institute of Engineering and Technology 🛎️Indian Institute of Information Technology Design & Manufacturing Kancheepuram, 🛎️Maharaja Agrasen Institute Of Technology, Delhi 🛎️Sarvam ,🛎️ NICT 🧙Paper Authors: Mohammed Safi Ur Rahman Khan, Priyam Mehta , Ananth Sankar , Umashankar Kumaravelan, Sumanth Doddapaneni, Suriyaprasaad G, Varun Balan, Sparsh Jain, Anoop Kunchukuttan, Pratyush Kumar, Raj Dabre, Mitesh Khapra 1️⃣Read the Full Paper here: https://lnkd.in/gqwQST-r 2️⃣Code: https://lnkd.in/giWPVntx 🎉 🎉  Congratulations to all the authors 🎉 🎉 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #ACL2024 #Indicllms #llms #llama2 #mixtral

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"ML Foundations: Matrix Inverses and Their Physical Meaning in Transformations" Linear algebra is a cornerstone of machine learning, and understanding concepts like matrix inverses can unlock deeper insights into how transformations work. In my latest lecture on Vizuara’s YouTube channel, "Foundations for Machine Learning | The Physical Intuition Behind Matrix Inverses," we explore the mechanics and intuition behind matrix inverses, determinants, and transformations: https://lnkd.in/gWqANmkd This lecture breaks down: 1) Linear transformations and inverse operations: -How a matrix inverse undoes the transformation of the original matrix. -Examples with transformations such as rotations, scaling, and shear. 2) The role of determinants: -When the determinant is non-zero, the inverse exists and the transformation is unique. -When the determinant is zero, the transformation squishes the space, creating no unique inverse. 3) Illustrative examples: -2D transformations are visualized as mapping unit vectors into parallelograms, showing how areas scale. -3D to 2D and other dimensional transformations that restrict the span of vectors. 4) When inverse matrices don't exist: -Scenarios where space is squished into a line or point, leading to infinite solutions for the same result vector. -Why such transformations lose critical information, making them non-reversible. This lecture is part of my larger Foundations for Machine Learning course—a 45-hour journey across 65 lectures that simplifies the core concepts for machine learning enthusiasts. The focus is always on building geometric and logical intuition, making abstract concepts more tangible and relatable. Join us to discover how understanding matrix inverses can reshape your perspective on machine learning and beyond. Watch the lecture now on Vizuara’s YouTube channel: https://lnkd.in/gWqANmkd

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Decode the enchanting tales of machine learning from industries 🧠📚🏭 🌟Netflix's Calibrated Recommendations youtu.be/DOWXNrBpO4w  🌟Netflix's Unified Recommendation ML Model: youtu.be/OKmv9sUrvk8 🌟Evolution of Recommendation Systems | Instagram Pinterest Twitter youtu.be/lgoyJn7MsH8 🌟Multi-Armed Bandit Startegies: youtu.be/2A5f3GrX0dA  🌟Overcoming Biases for a Better Recommender System | How Tech-Titans Combat Recommender System Biases youtu.be/oGb_mIdO0tA 🌟Building Scalable Query-Item Two-Tower Model based Retrieval System youtu.be/o-pZk5R0TZg  🌟Meituan's Game-Changing Two-Tower Retrieval Recsys Model | KDD'21 youtu.be/UhpbTSbi3lI  🌟 Innovative Linkedin's Deep-leaning based CTR Modeling: The Deep, Wide, and Shallow Towers Explained youtu.be/7l0HLYVFEuU 🌟 Lessons from Instagram’s Cutting-Edge Recommendation System youtu.be/Myna6rnmCG8 🌟 How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm 🐦 youtu.be/IhGq9jgcxFM 🌟 How eBay Built a Next-Generation Language Model to Revolutionise Online Shopping 🛒 youtu.be/h51nbWr7feo 🌟 Leveraging Pre-trained Bert to generate Entity Embeddings across domain 📚 youtu.be/v-0J7o-nDBE 🌟 Uplift Modeling to Detect Causal Effect | Uplift Modeling & Causal Inference to Improve Instagram ✨youtu.be/rKzG0Ct_ReA 🌟 Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform 📌youtu.be/KEiBJ0fal3Q 🌟 Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains 💰youtu.be/a_CXpnsvPa0 🌟 Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU 💡youtu.be/DN4S96oHRhE 🌟 Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS 📊 youtu.be/Xqo8fwgjxW4 🌟 Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer 🔍youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U 🌟 PID Controller to add a layer of diversity to the recommender systems 🎛️ youtu.be/laTxgnzjfR0 #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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🚨Paper Alert 🚨 ➡️Paper Title: A Multimodal Automated Interpretability Agent 🌟Few pointers from the paper 🎯In this paper authors have described MAIA, “a  🤖 Multimodal Automated Interpretability Agent 🤖”. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. 🎯It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. 🎯These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results. 🎯Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior. 🎯Authors evaluated  applications of MAIA to computer vision models. They first characterize MAIA’s ability to describe (neuron-level) features in learned representations of images. 🎯Across several trained models and a novel dataset of synthetic vision neurons with paired ground-truth descriptions, MAIA produces descriptions comparable to those generated by expert human experimenters. 🎯Then Authors showed that MAIA can aid in two additional interpretability tasks: reducing sensitivity to spurious features, and automatically identifying inputs likely to be mis-classified. 🏢Organization:  MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) 🧙Paper Authors: Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas , Antonio Torralba 1️⃣Read the Full Paper here: https://lnkd.in/gjmN_gaR 2️⃣Project Page: https://lnkd.in/gxPr_jpM 3️⃣Experiment Browser: https://lnkd.in/gGe7FyUY 🎥 Be sure to watch the attached Demo Video-Sound on 🔊🔊 Music by Sergii Pavkin from Pixabay Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

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📚 Book Review: "Mastering PyTorch" – Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond by Ashish Ranjan Jha 🚀 I recently finished reading "Mastering PyTorch," and I must say it is an excellent resource for anyone looking to delve deep into the world of deep learning using PyTorch. Whether you are a data scientist, machine learning researcher, or deep learning practitioner, this book offers a wealth of knowledge and practical insights to enhance your skills and understanding. 🔑 Key Highlights: 1️⃣ Comprehensive Overview: The book kicks off with an introduction to deep learning and PyTorch, setting a strong foundation for beginners and experienced practitioners alike. 2️⃣ Advanced CNN and RNN Architectures: It covers SOTA advancements in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), providing hands-on examples and practical applications. 3️⃣ Transformers and Hybrid Models: The exploration of transformers and hybrid models is particularly insightful, showcasing how these models have revolutionized natural language processing. 4️⃣ Graph Neural Networks (GNNs): A detailed dive into GNNs using PyTorch Geometric, explaining the concepts and implementations of Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). 5️⃣ Generative Models: The sections on music and text generation, neural style transfer, GANs, and diffusion models are fascinating, demonstrating the creative potential of deep learning. 6️⃣ Deep Reinforcement Learning: Practical examples of training reinforcement learning agents in PyTorch add another dimension to the book's versatility. 7️⃣ Model Deployment: Step-by-step guidance on deploying PyTorch models into production, including mobile deployment for Android and iOS, is incredibly valuable for real-world applications. 8️⃣ Optimization and AutoML: Chapters on model training optimizations, distributed training, and AutoML provide techniques to efficiently train large models and automate the ML workflow. 9️⃣ Explainable AI: The focus on interpretability using tools like Captum ensures that the models are not just powerful but also understandable. 🔟 Integration with Hugging Face: Leveraging Hugging Face libraries with PyTorch to build cutting-edge AI models is a perfect ending to this comprehensive guide. If you have hands-on experience with Python and a basic understanding of PyTorch, this book is for you. It caters to those who wish to implement advanced deep learning paradigms and need practical examples to bridge the gap between theory and practice. 👨‍⚖️ "Mastering PyTorch" is more than just a technical guide; it is a gateway to mastering deep learning with one of the most powerful frameworks available today. 🌟 As an optim fan, Chapters 12, 13, 15 and 16 specially recommended! #DeepLearning #PyTorch #MachineLearning #AI #DataScience #BookReview #Transformers #ReinforcementLearning

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When it comes to vector operations, two concepts that are often confused with each other are Dot Product and Cosine Similarity. While they may seem similar at first glance, the key difference lies in how they handle magnitude. 𝘋𝘰𝘵 𝘗𝘳𝘰𝘥𝘶𝘤𝘵: 𝘪𝘵 𝘮𝘦𝘢𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦 𝘦𝘹𝘵𝘦𝘯𝘵 𝘵𝘰 𝘸𝘩𝘪𝘤𝘩 𝘵𝘸𝘰 𝘷𝘦𝘤𝘵𝘰𝘳𝘴 𝘢𝘳𝘦 𝘱𝘰𝘪𝘯𝘵𝘪𝘯𝘨 𝘪𝘯 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘥𝘪𝘳𝘦𝘤𝘵𝘪𝘰𝘯, 𝘤𝘰𝘯𝘴𝘪𝘥𝘦𝘳𝘪𝘯𝘨 𝘵𝘩𝘦𝘪𝘳 𝘭𝘦𝘯𝘨𝘵𝘩. 𝘊𝘰𝘴𝘪𝘯𝘦 𝘚𝘪𝘮𝘪𝘭𝘢𝘳𝘪𝘵𝘺: 𝘵𝘢𝘬𝘦𝘴 𝘵𝘩𝘦 𝘥𝘰𝘵 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘢𝘯𝘥 𝘥𝘪𝘷𝘪𝘥𝘦𝘴 𝘪𝘵 𝘣𝘺 𝘵𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘰𝘧 𝘵𝘩𝘦 𝘮𝘢𝘨𝘯𝘪𝘵𝘶𝘥𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘸𝘰 𝘷𝘦𝘤𝘵𝘰𝘳𝘴. 𝗔 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 Let's consider three vectors representing sentences: s1 = [1, 2, 1] ("The cat sat on the mat.") s2 = [1, 1, 1] ("The cat is sitting on the mat.") s3 = [4, 5, 6] ("The cat is sitting comfortably on the warm mat in the living room near the fireplace.") Dot product between s1 and s2: 4 Dot product between s1 and s3: 20 (large difference due to magnitude) Cosine Similarity between s1 and s2: 0.942 Cosine Similarity between s1 and s3: 0.930 (smaller difference, focusing on direction) 𝗦𝗼, 𝘄𝗵𝗲𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗲𝗮𝗰𝗵? Dot Product is suitable for applications like search engines, where the relevance of a document to a query is determined by the frequency of key terms (longer vectors naturally have higher dot products). Other examples can be, in recommendation systems to measure similarity between users or between items etc. Cosine Similarity is ideal for comparing the similarity between documents, sentences, or word embeddings in NLP tasks, as it normalises for length and focuses on direction. Other examples can be, analyzing connections or interactions in social networks, retrieving similar images based on feature vectors etc. #DotProduct #CosineSimilarity #VectorOperations #SearchEngines #NLP #DocumentSimilarity #SentenceSimilarity #SocialNetworkAnalysis #ImageRetrieval #MachineLearning #DataScience #Mathematics #Algorithms #DataAnalysis

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"Here is a detailed 5-phase roadmap if you wish to transition to AI/ML" Stepping into ML can be difficult, but a clear roadmap can make all the difference. The only resource you need: Vizuara’s YouTube playlists. Phase 1️⃣: Mathematical Foundations (20-25 hours) Playlist 1: Foundations for ML: https://lnkd.in/gKz-eybU -Why Begin Here: Grasp the basics- Linear algebra, Probability, Statistics, Calculus, Optimization, Programming fundamentals -Commitment: 2-3 hours weekly for 8 weeks. Phase 2️⃣: Machine Learning (60-65 hours) 📌Playlist 1: ML Teach by Doing: https://lnkd.in/gn2dEcE2 -Why It’s Important: Practical, project-based learning to understand ML workflows. -Commitment: 4 hours weekly for 10 weeks. 📌Playlist 2: Decision Trees from Scratch: https://lnkd.in/g3cmj2BR -Why It’s Useful: Master decision tree algorithms are the backbone of many ML models. -Commitment: 4 hours weekly for 5 weeks. Phase 3️⃣: Deep Learning (35-40 hours) 📌Playlist 1: Neural Networks from Scratch: https://lnkd.in/gj8kHe2T -Why It Matters: Understand the mechanics of neural networks through implementation. -Commitment: 5 hours weekly for 8 weeks. Phase 4️⃣: Advanced topics: Graph Neural Networks (40-45 hours) 📌Playlist 1: Graph Neural Networks - Theory, Applications and Research: https://lnkd.in/g3RCPS8e -Why Learn This: Graph-based ML is becoming increasingly relevant in fields like social networks and biology. -Commitment: 3 hours weekly for 8 weeks. 📌Playlist 2: ML Project-Based Course: Explainable AI: https://lnkd.in/gNEx3ghr Why XAI?: Build ML projects with a focus on interpretability -Commitment: 3 hours weekly for 5 weeks. -Outcome: Publish your first research paper using XAI techniques. Phase 5️⃣: Generative AI, Transformers, and LLMs (100-110 hours) 📌Playlist 1: GenAI for Beginners (8 hours): https://lnkd.in/gUgXxVzh 📌Playlist 2: LLMs from scratch (40-45 hours): https://lnkd.in/gjcyfCcE 📌Playlist 3: Hands-on LLMs (40-45 hours): https://lnkd.in/gJQ7ryE4 📌Playlist 4: Transformers (15 hours): https://lnkd.in/g_3Qdu6d -Why These Topics?: Learning about LLMs, transformers, and generative AI will make you future-ready. -Commitment: 5 hours weekly for 20 weeks. 🔸Optional [140 hours] 📌Introduction to Machine Learning in Julia [40 hours]: https://lnkd.in/g8A3DtQW 📌Zero to Hero in Data Science [40 hours]: https://lnkd.in/gNEgx2Cz 📌Hands-on PINN [20 hours]: https://lnkd.in/gta5hgHZ 📌ML in Hindi [40 hours]: https://lnkd.in/giD88GzZ *** ✅Total Duration: 275 hours + optional 140 hours ✅Timeline: 6-8 months, balancing learning with practical application. ✅Outcome: Build foundational ML knowledge, gain practical skills, and stay ahead with advanced topics. *** If you are willing to spend time, this roadmap will help you get there. Follow Vizuara’s YouTube channel for structured and beginner-friendly playlists: https://lnkd.in/g455AJVw Your ML journey begins now—start building your expertise today.

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Learn Data Science from these hands-on videos. 🌟PySpark Essentials Practical Guide youtu.be/aruptWppgSs 🌟The Making of SRKGPT: Crafting an AI with Shahrukh Khan's Style LLama v2 Finetuning youtu.be/gYPwx0DR7zc   🌟Building Scalable Query-Item Two-Tower Model based Retrieval System youtu.be/o-pZk5R0TZg 🌟 Enhancing the performance of smaller open source LLM by training on larger LLM dataset like Chat-GPT youtu.be/paGr-t1wSOQ youtu.be/lo11Iczb0Vc  🌟 Finetuning Falcon & LLama-2 LLM’s to custom Datasets  ​​youtu.be/CxqZ5j3xlt0 youtu.be/8cc4bJtycOA 🌟 Leveraging Pre-trained Bert to generate Entity Embeddings across domain: youtu.be/v-0J7o-nDBE 🌟Model Compression with Knowledge Distillation: youtu.be/1N_EBJUOjVU  🌟Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains: youtu.be/a_CXpnsvPa0 🌟 Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU: youtu.be/DN4S96oHRhE 🌟 Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS: youtu.be/Xqo8fwgjxW4 🌟 How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm youtu.be/IhGq9jgcxFM 🌟 Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform: youtu.be/KEiBJ0fal3Q 🌟 Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer: youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U 🌟 PID Controller to add a layer of diversity to the recommender systems: youtu.be/laTxgnzjfR0 🌟 Train Neural Networks to Approximate any Function youtu.be/4PvGKuqRQTE 🌟 Dimensionality Reduction Techniques: PCA versus AutoEncoders, A comparison through Image Dataset https://lnkd.in/gC6XQfez #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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Learn Data Science from these hands-on videos. 🌟PySpark Essentials Practical Guide youtu.be/aruptWppgSs 🌟The Making of SRKGPT: Crafting an AI with Shahrukh Khan's Style LLama v2 Finetuning youtu.be/gYPwx0DR7zc   🌟Building Scalable Query-Item Two-Tower Model based Retrieval System youtu.be/o-pZk5R0TZg 🌟 Enhancing the performance of smaller open source LLM by training on larger LLM dataset like Chat-GPT youtu.be/paGr-t1wSOQ youtu.be/lo11Iczb0Vc  🌟 Finetuning Falcon & LLama-2 LLM’s to custom Datasets  ​​youtu.be/CxqZ5j3xlt0 youtu.be/8cc4bJtycOA 🌟 Leveraging Pre-trained Bert to generate Entity Embeddings across domain: youtu.be/v-0J7o-nDBE 🌟Model Compression with Knowledge Distillation: youtu.be/1N_EBJUOjVU  🌟Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains: youtu.be/a_CXpnsvPa0 🌟 Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU: youtu.be/DN4S96oHRhE 🌟 Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS: youtu.be/Xqo8fwgjxW4 🌟 How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm youtu.be/IhGq9jgcxFM 🌟 Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform: youtu.be/KEiBJ0fal3Q 🌟 Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer: youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U 🌟 PID Controller to add a layer of diversity to the recommender systems: youtu.be/laTxgnzjfR0 🌟 Train Neural Networks to Approximate any Function youtu.be/4PvGKuqRQTE 🌟 Dimensionality Reduction Techniques: PCA versus AutoEncoders, A comparison through Image Dataset https://lnkd.in/gC6XQfez #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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𝐒𝐐𝐋 𝐑𝐞𝐟𝐫𝐞𝐬𝐡𝐞𝐫 𝐚𝐧𝐝 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 (𝐏𝐚𝐫𝐭-𝟖)🍃 𝐒𝐐𝐋 𝐉𝐨𝐢𝐧𝐬 : A key to Relational Data Analysis Joins allows to combine data from multiple tables in a relational database, unlocking deeper insights from the datasets. 𝐓𝐡𝐞 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐝𝐢𝐯𝐞𝐬 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐣𝐨𝐢𝐧𝐬 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬. 𝐓𝐡𝐞𝐬𝐞 𝐚𝐫𝐞 𝐚𝐬 𝐟𝐨𝐥𝐥𝐨𝐰𝐬: 1. Inner Join 2. Left Join 3. Right Join 4. Full Outer Join 5. Cross Join 6. Self Join 𝐒𝐨𝐦𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐩𝐨𝐢𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫𝐞𝐝 𝐰𝐡𝐢𝐥𝐞 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐉𝐨𝐢𝐧𝐬: ·  Understanding the core functionalities of every join type. ·  Defining clear join conditions to specify how rows from tables should be matched based on compatible columns. ·  Watching out for ambiguity in complex join queries. ·  Complex joins can be slow. Optimize if needed. ·  Left and full outer joins introduce NULLs. Be prepared to address them. ·  Choosing the right join type, inner joins for matches, left/right for including all records from one table, full outer for all data regardless of matches. ·  Mostly left and inner joins are being used. ·  Considering subqueries or CTEs for complex joins to improve readability or performance. Attaching a SQL Joins Cheatsheet for reference too. Grateful to Zain Ul Hassan for the invaluable mentorship he has been providing since the starting. Also appreciating Rishabh Mishra for providing concise and informative content for learning.

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Glad to share that we have 𝐓𝐰𝐨 𝐏𝐚𝐩𝐞𝐫𝐬 accepted by #ICML2024, related to 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬, 𝐋𝐋𝐌, 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬, 𝐒𝐃𝐄, etc.: [1] What Can Large Language Models Tell Us about Time Series Analysis, 𝐚𝐫𝐗𝐢𝐯: https://lnkd.in/grihztTj [2] BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition, 𝐚𝐫𝐗𝐢𝐯: https://lnkd.in/gz9cDBrh In the 1st paper, we argue that current LLMs have the potential to revolutionize time series analysis, thereby promoting efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Such advancement could unlock a wide range of possibilities, including modality switching and time series question answering. We encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. Furthermore, we detail the seamless integration of time series analysis with existing LLM technologies and outline promising avenues for future research. In the 2nd paper, we propose BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition. We treat the multivariate time series as the weighted combination of groups of low-rank temporal factors with different patterns. We apply a group of Gaussian Processes (GPs) with different kernels as functional priors to fit the factors. For computational efficiency, we further convert the GPs into a state-space prior by constructing an equivalent stochastic differential equation (SDE), and developing a scalable algorithm for online inference. The proposed method can not only handle imputation over arbitrary time stamps, but also offer uncertainty quantification and interpretability for the downstream application. We evaluate our method on both synthetic and real-world datasets. Credits to all co-authors: Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan SHIKAI FANG, Yingtao Luo, Shandian Zhe, Liang Sun #icml #icml2024 #timeseries #llm #imputation #forecasting #machinelearning #deeplearning #datamining #sde

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#Transformers the building block on #GenAI, 3 resources which help me to understand : 1. Krish Naik : (https://lnkd.in/g5yF66Fa) 2. Josh Starmer : (https://lnkd.in/gjeY6MyQ) 3. Blog by Jay : https://lnkd.in/gYxxcvDD Krish Naik Joshua Starmer PhD Jay Alammar #MachineLearning #FridayMotivation

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Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess textual features providing context. Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. With the rise of Large Language Models (LLMs), a trend has emerged, integrating LLMs with GNNs to tackle diverse graph tasks and enhance generalization capabilities through self-supervised learning methods. The rapid evolution and immense potential of Graph ML pose a need for conducting a comprehensive review of recent advancements in Graph ML. #neuralnetworks #artificialintelligence #ai #graphneuralnetworks #llms #generativeai #deeplearning #machinelearning

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