Bunty Shah

𝐎𝐧𝐥𝐲 𝐨𝐧𝐞 𝐠𝐮𝐢𝐝𝐞 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐡𝐨𝐰 𝐀𝐈 𝐭𝐫𝐮𝐥𝐲 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐬 𝐦𝐞𝐚𝐧𝐢𝐧𝐠. This Embeddings Guide breaks down the hidden layer of intelligence that powers modern AI from search and retrieval to reasoning and recommendations. Here’s what it covers: • Types of Embeddings → Dense, Sparse, Multi-Vector, Long Context, and Code • Performance Metrics → How to evaluate embedding quality and retrieval accuracy • Cost Insights → Comparison of top providers like OpenAI, Cohere, and Hugging Face • Real-World Use Cases → Question answering, semantic search, and conversational AI • Optimization Tips → Balancing vector dimensions, latency, and storage efficiency If you’re building RAG pipelines, optimizing LLM search, or designing AI systems, this guide helps you see how machines think — in vectors. ➕ Follow Naresh Edagotti for more content that makes complex AI topics feel simple.

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Random search is a simple yet effective optimization method that selects random samples from the parameter space to find the best configuration. It is widely used in machine learning and data science due to its flexibility and ability to address complex, non-linear problems. ✔️ Easy to implement: Random search avoids complex gradient calculations, making it suitable for a wide range of optimization tasks. ✔️ Efficient in large spaces: Compared to grid search, it focuses on randomly chosen subsets, reducing computational cost while covering diverse regions. ✔️ Handles high-dimensional problems: Scales well to problems with many parameters, making it particularly effective for hyperparameter tuning. ❌ Computational inefficiency in some cases: In very large or highly constrained spaces, random sampling may require extensive iterations to find an optimal solution. ❌ Uninformed exploration: Random sampling does not utilize information from past samples, potentially leading to slower convergence compared to more guided methods. 🔹 Alternatives to consider: Bayesian optimization and genetic algorithms build on previous iterations to guide sampling, often achieving better results with fewer evaluations. The visualization below illustrates how random search and other methods approach optimization. Methods like random search (1 & 2) do not require gradients, while algorithms such as Gauss-Newton (3) depend on gradients for efficient exploration. This distinction highlights random search’s flexibility but also its limitations in structured spaces. Source: https://lnkd.in/egV6MzM2 🔹 In Python: Use RandomizedSearchCV in scikit-learn for hyperparameter optimization, or scipy.optimize for general optimization. Advanced libraries like optuna or hyperopt offer more efficient approaches while retaining randomness. 🔹 In R: The caret package supports random search for hyperparameter tuning through trainControl. Alternatively, mlr3 and tidymodels provide robust frameworks for implementing random search strategies. For more insights and tools to improve your data science skills, join my email newsletter on Statistics, Data Science, R, and Python! Learn more: http://eepurl.com/gH6myT #research #dataanalytics #data #rstats #bigdata #package

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🚨 #Top Interview Questions for Data Science & #GenAI Freshers: GraphRAG, Hybrid Pipelines & Deep Thinking 🚨 #Part – 44 #GraphRAG vs #VectorRAG — Decision Thinking 1. If your company knowledge base has “who reports to whom” and policy dependencies, why might GraphRAG outperform VectorRAG? 2. If every document node in your GraphDB has embeddings attached, are you doing GraphRAG or HybridRAG? 3. Why might Graph traversal (neighbors) sometimes retrieve more relevant context than semantic similarity search? 4. If a question requires combining info from multiple connected entities, how will a pure vector RAG fail? 5. What’s the main risk of building your graph from LLM-extracted entities without human validation? #Retrieval & Ranking Challenges 6. If your retriever returns 10 chunks and 8 come from the same doc, is that a retrieval success or failure? 7. How would you handle diversity vs relevance in chunk ranking? 8. If your user question contains multiple intents (e.g., “Summarize Q1 revenue and suggest improvements”), would you use single retriever call or multi-query retriever? 9. Why might a query transformer agent improve retrieval more effectively than just increasing top-k? 10. If your reranker consistently demotes relevant chunks, where will you debug — embeddings, metadata, or prompt design? #Complex Document RAG 11. Your documents contain text, charts, and tables, will you embed visual data separately or describe it in text form? 12. How will you chunk a PDF where table headers repeat every page? 13. If one paragraph references a figure on another page, how do you ensure context linking during retrieval? 14. Would you treat extracted table cells as independent chunks or as a structured JSON node? Why? 15. For multilingual contracts, will you translate to English first or embed in native language? Which is safer for retrieval accuracy? #Agentic Decision-Making 16. If your Router Agent always routes queries to the SQL path, even for policy questions, what’s the likely cause? 17. How would you prevent two agents from looping or overwriting each other’s context in a multi-agent pipeline? 18. What signals can an LLM agent use to decide between retrieval vs reasoning mode? 19. How can metadata weighting (like recency or confidence score) improve an agent’s decision-making? 20. If your Answer Maker Agent is correct but the SQL Executor Agent gives partial data, where do you intervene first prompt, data, or flow logic? #Quote for Freshers: “Every #RAG pipeline looks the same… until it breaks. The difference between an average #fresher and a real GenAI engineer is knowing where and why it breaks.” 📩 #DM for guidance if you’re stuck or unable to answer such scenario questions in #interviews — I’ll help you build real #understanding, not just theory. #GenAI #GraphRAG #VectorRAG #HybridRAG #Agents #LLM #RAG #AI #DataScience #InterviewPrep #Freshers #Part44 #Retrieval #Chunking #Embedding #Debugging #KnowledgeGraphs

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Here is the updated version of the compiled list of Machine Learning videos on my channel grouped under topics focusing on both theoretical and industry-level practical use cases. 🌟 Data Science Essentials 📌 Categorical Encoding youtu.be/MKuAQv6ybc8 📌 Math for ML youtu.be/oMY2uKjx_Zc 📌 Field Buzzwords youtu.be/maxyUZGB3QY 📌 Interview Trends youtu.be/XrZXojl5Zd8 📌 Linear Regression lnkd.in/gdRsMHbn 📌 Logistic Regression lnkd.in/gtPfmQUv 📌 Classification Metrics youtu.be/E2HRSJKU-_4 📌 Naive Bayes youtu.be/IvTCdrx1SHQ 📌 Variable Types youtu.be/Y6PEpkEdXDQ 📌 Dimensionality Reduction lnkd.in/gC6XQfez 📌 Entropy & KL lnkd.in/gSXh8ZX8 📌 Dynamic Pricing youtu.be/a_CXpnsvPa0 🌟 Recommendation Systems 📌 Uber Eats Bias youtu.be/ZCO75OuMRY0 📌 Pinterest Ranking youtu.be/WQj8QtjC3gA 📌 Netflix Recommendations youtu.be/DOWXNrBpO4w 📌 Netflix Unified ML youtu.be/OKmv9sUrvk8 📌 Recsys Evolution youtu.be/lgoyJn7MsH8 📌 Training Embeddings youtu.be/DN4S96oHRhE 📌 Annoy for ANN youtu.be/DSQOrBTqmYA 📌 Product Quantizer youtu.be/50PNumB7s3U 📌 Account Recs youtu.be/Xqo8fwgjxW4 📌 PID for Diversity youtu.be/laTxgnzjfR0 📌 Instagram Recs youtu.be/Myna6rnmCG8 📌 LinkedIn CTR youtu.be/7l0HLYVFEuU 📌 Meituan Two-Tower youtu.be/UhpbTSbi3lI 📌 Query-Item Model youtu.be/o-pZk5R0TZg 📌 Twitter Recs youtu.be/IhGq9jgcxFM 📌 eBay Language Model youtu.be/h51nbWr7feo 📌 Bias Overcome youtu.be/oGb_mIdO0tA 📌 Uplift Modeling youtu.be/rKzG0Ct_ReA 📌 Spotify GNNs youtu.be/7lCt5JD9VJA 🌟 Advanced Models 📌 Model Calibration youtu.be/rG2EfFOXyg0 📌 Data Drift youtu.be/tQjRQWfYQ10 📌 Neural Approximation youtu.be/4PvGKuqRQTE 📌 Pinterest Ads youtu.be/KEiBJ0fal3Q 📌 Entity Embeddings youtu.be/v-0J7o-nDBE 📌 Knowledge Distill youtu.be/1N_EBJUOjVU 📌 Multi-Armed Bandits youtu.be/2A5f3GrX0dA 📌 Intro to GANs youtu.be/KUeq-wszG80 📌 PySpark Basics youtu.be/aruptWppgSs 📌 LinkedIn Pacing youtu.be/R4EZ92VJvSI 📌 Returns Fraud youtu.be/as4i1tUo0EA 📌 E-Com Fraud youtu.be/YQZBgvLB_EQ 🌟 Large Language Models 📌DeepSeek R1: The $6M AI youtu.be/CkIVqoMIxsw 📌GenAI powers Uber, Yelp & more youtu.be/9w8_volSm6w 📌GenAI powers Businesses youtu.be/SPR_-GXrZ2E 📌 Coding AI Agent youtu.be/dsbjMbK_9Gw  📌 RAG with LLMs youtu.be/1JFPindItz4 📌 Smaller LLaMA youtu.be/-JXfbJv7lic 📌 Generative AI youtu.be/PPl0MRuCKLo 📌 Chat-GPT Tech youtu.be/JKoJ5YIr2O4 📌 Conversational LLMs youtu.be/MHfzoHC4kek 📌 LLM Optimizations youtu.be/mF7OM_XU2S4 📌 LLM Finetuning youtu.be/lo11Iczb0Vc 📌 SRK’s LLaMA youtu.be/gYPwx0DR7zc #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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One of the most asked questions in data science interviews: What are activation functions? A mathematical “gate” that decides whether a neuron should be activated, transforming weighted inputs into outputs for the next layer. Key Roles: Decision Maker Determines if neuron input is important for prediction Transformer Converts summed weighted inputs into meaningful outputs Backprop Helper Enables gradient calculation for weight adjustments Network Bridge Connects current neuron output to next layer input 3 Main Types Binary Step Function Threshold-based activation - neuron fires only if input exceeds set value Linear Function Direct proportional output (f(x) = x) - essentially no activation Non-Linear Functions Enables complex mappings and deep learning capabilities In tomorrow’s post, let’s understand what are the different Non-Linear activation functions and when to use each one. Which activation function do you think performs best for image classification tasks? Drop your thoughts below! Follow Akash Kamerkar for regular updates, job postings, and valuable data science content. 𝐈𝐧𝐬𝐭𝐚: https://lnkd.in/g9nWMkni 𝐘𝐨𝐮 𝐓𝐮𝐛𝐞: https://lnkd.in/gd-7FC4F 𝐉𝐨𝐢𝐧 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄 (link in comments) and stay updated with the latest opportunities, guidance, and expert advice.

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🚀 Want to become a Data Scientist in 2025? Then you need more than just coding skills - you need to ace the interview questions that every company asks. Here’s why most candidates fail: ❌ They focus only on coding and ignore statistics ❌ They can’t explain ML models in simple terms ❌ They struggle with SQL & real-world case studies That’s where this Data Scientist Interview Q&A Guide comes in. It covers: ✅ Core Statistics & Probability questions you can’t skip ✅ Python & SQL concepts for data handling ✅ Machine Learning theory + real-life applications ✅ Case studies & scenario-based problem solving ✅ Communication tips to explain models like a pro Whether you’re aiming for FAANG, startups, or analytics-driven companies, these Q&A will give you the confidence and clarity to stand out. Your next opportunity might just depend on how well you prepare today. 🔁 Repost & Share with others to help them upskill! 📢 Follow Sakshi Singh Kushwaha for more such content. #DataScience #InterviewPreparation #CareerGrowth #AI

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