Opytimizer Python Library for Hyperparameter Optimization

🚀 New Release: NTQR Open Source Python Package I’m excited to share the latest release of NTQR, a Python package designed for those working at the intersection of AI safety, scalable oversight, and formal verification. NTQR provides a formal framework for reasoning about systems where ground truth is unknown—an increasingly relevant constraint when supervising or composing advanced AI systems. If you’re thinking about verifier reliability, adversarial reporting, or Gödel/Löb-style limits in oversight architectures, this package is built with you in mind. 🔍 What’s new Improved classes for constructing sample statistics variables and their axioms. Executable Jupyter notebooks that demonstrate the logic and its algebra. Clearer abstractions for computing possible and consistent evaluation sets. 📦 Get started in minutes pip install ntqr cd <your-working-directory> ntqr-docs cd ntqr_notebooks jupyter notebook This will install the package and generate a local set of executable notebooks that: Introduce the algebra behind the counting logic Demonstrate key constructions Demonstrate no-knowledge alarms for misaligned classifiers 💡 Why this matters As AI systems become more capable, oversight itself must scale—often through other AI systems. But this introduces a core problem: what happens when the systems we rely on for verification are not fully trustworthy or we do not know the ground truth? When AI judges monitor other AIs they are often acting as classifiers. Who judges the judges? NTQR helps you make them monitor themselves. NTQR offers a way to: Treat unsupervised evaluation as a logical problem. Infer group evaluations that match the observed agreement and disagreement counts between classifiers, the logically consistent evaluations. Construct no-knowledge alarms for misaligned classifiers using only the counts of how they agree and disagree on a test. If you’re exploring alignment, verification, or theoretical limits of monitoring systems, I’d be very interested in your feedback. 📚 Docs: https://lnkd.in/eugreNDd #AISafety #ScalableOversight #Alignment #FormalMethods #MachineLearning #Jupyter #Python

Just came across something interesting — Google dropped a new library called LangExtract. It’s a Python tool that basically takes unstructured documents and turns them into structured data with just a few lines of code. No complicated setup. What I found genuinely useful: - It maps every extracted piece back to where it came from in the document - Keeps outputs consistent with defined schemas - Can handle long documents using parallel processing - Generates HTML visualizations to actually see what’s happening - Works with Gemini, Ollama, and even open-source models - Doesn’t feel tied to one specific use case — pretty flexible Also, it’s open source. No API keys, no usage limits. Feels like something that could simplify a lot of LLM and document processing workflows. Here’s the link if you want to check it out: https://lnkd.in/gNKBKNwx #AI #Python #OpenSource #LLM #GenAI

Shipped: Python SDK for tag-graph agent memory. For a year I've been chasing one problem — how do you give an LLM agent memory that's bounded, predictable, and doesn't blow your token bill? Vector DBs → fuzzy, impossible to budget. Raw history → 5-turn context overflow. Summarize-and-re-inject → silently drops facts the agent needs three turns later. So we built MME — a bounded tag-graph memory engine. Every memory carries tags, retrieval starts from the current scope, propagates to neighbors with bounded fanout, ranks by graph proximity. Deterministic, token-budgeted, sub-50ms at 100k items. Today the Python SDK is live: → pip install railtech-mme → Native LangChain + LangGraph tool integrations → Online learning via feedback loops → Open source Wrote up the full design rationale, tradeoffs vs. vector search, and the SDK surface area here: https://lnkd.in/eNR5n_iq Honest beat — this is launch day. If you're building LLM agents in Python and "my agent doesn't remember things well" feels familiar, I'd love to hear what's clunky about the API. #AI #Python #LangChain #LLM #AgentMemory #BuildInPublic #OpenSource

AI Beyond the Hype | Part 8: Vector Databases “What is Python used for?” “Is python dangerous?” Same word. Completely different meaning. 👉 In one case → Python = programming language 🧑💻 👉 In another → python = reptile 🐍 We can’t store every possible variation or phrasing. Traditional search fails here because it works on exact match, not meaning. This is where semantic search (search based on meaning) comes in — and that’s where vector databases play a key role. ## 🧠 What is a Vector Database? A vector DB stores data as embeddings (numbers) instead of plain text, so it can search based on meaning. ## 🔢 How data is generated and stored Text → tokens → embeddings Example: “Python is used for backend development” → [0.12, -0.45, 0.78, …] “Python is a dangerous reptile” → [-0.33, 0.91, -0.12, …] These numbers capture meaning, not just words. ## 🔍 How search happens User query → embedding Example: “Python coding” → vector “Is python poisonous” → vector Then system finds vectors that are closest in meaning (not exact match). This is semantic search. ## ⚡ How search is optimized Searching millions of vectors directly is slow. So vector DBs use indexing (ANN – Approximate Nearest Neighbors) and sometimes hashing/partitioning to find nearest vectors quickly. ## 🧩 How prompt-based retrieval works 1. Query → embedding 2. Retrieve relevant chunks 3. Add to prompt 4. LLM generates answer → This is how RAG works internally. ## 🚨 Reality check Vector DB doesn’t understand meaning. It just finds patterns that are mathematically close. ## ⚠️ Challenges Similar ≠ correct Bad embeddings → bad retrieval Needs tuning (top-k, thresholds) Scaling & latency trade-offs ## 💡 Takeaway 👉 “Vector DB doesn’t search words — it searches meaning.” Funny how things work — what felt pointless in school is now the backbone of AI systems

This Python tool just made vector databases optional for RAG. It's called PageIndex. It reads documents the way you do. No embeddings. No chunking. No vector database needed. # Here's the problem with normal RAG: It takes your document, cuts it into tiny pieces, turns those pieces into numbers, and searches for the closest match. But closest match doesn't mean best answer. # PageIndex works completely different. → It reads your full document → Builds a tree structure like a table of contents → When you ask a question, the AI walks through that tree → It thinks step by step until it finds the exact right section Same way you'd find an answer in a textbook. You don't read every page. You check the chapters, pick the right one, and go straight to the answer. That's exactly what PageIndex teaches AI to do. Here's the wildest part: It scored 98.7% accuracy on FinanceBench. That's a test where AI answers real questions from SEC filings and earnings reports. Most traditional RAG systems can't touch that number. Works with PDFs, markdown, and even raw page images without OCR. 100% Open Source. MIT License.

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆𝘀 𝗩𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝘀𝘁𝘀 You use NumPy arrays often. You might wonder why you need them. Python lists hold numbers. Python lists support indexing. Speed is the main reason. Testing 5 million numbers shows a huge gap. A Python list takes 0.83 seconds. A NumPy array takes 0.0089 seconds. NumPy is 94 times faster. This gap grows with more data. Memory is the secret. Python lists store references to objects. These objects are scattered. To multiply a list, Python visits each object one by one. NumPy arrays store raw numbers in one block. All elements have the same type. NumPy uses C code to process these in parallel. Packing wins. Fixed types provide speed. - int8 uses 1 byte per number. - int64 uses 8 bytes per number. Using int8 saves 8 times more memory. This helps you fit large datasets into RAM. Deep learning models use float32 to save GPU memory. Useful NumPy tools: - linspace: Creates evenly spaced numbers. - Fancy indexing: Picks specific rows without loops. - Boolean masking: Filters data in one line. - Broadcasting: Adds arrays of different shapes. Essential functions: - sum, mean, and std: Fast statistics. - argsort: Finds the rank of items. - vstack and hstack: Combines data matrices. Now you know NumPy. Next is Pandas. Pandas handles labels and messy real world data. Source: https://lnkd.in/gVMVwUyC Optional learning community: https://t.me/GyaanSetuAi

Need YouTube transcripts for GenAI or RAG? Use a tiny Python script to pull them straight from a URL. 🐍📹 The post shows how to use youtube_transcript_api to extract a video’s ID, fetch the transcript (list of dicts with text/start/duration), and convert it into clean text for LLMs. - pip install youtube_transcript_api ✅ - Extract the video_id from the watch?v= URL - Fetch transcript (returns text, start, duration) - Join the text segments into readable transcript for summarization/RAG Why read: quick, reproducible steps to turn any YouTube video into usable text for GenAI, SEO, or accessibility. Read more: https://lnkd.in/eccX_UY5 #Python #GenAI #RAG #YouTube #transcript

Workflow Experiment Tracking using livelossplot #machinelearning #datascience #workflowexperimenttracking #livelossplot A live training loss plot in Jupyter Notebook for Keras, PyTorch and other frameworks. An open-source Python package by Piotr Migdał, Bartłomiej Olechno and others. Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting? Visual feedback allows us to keep track of the training process. Now there is one for Jupyter. If you want to get serious - use TensorBoard, But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using livelossplot as a plug&play component https://lnkd.in/dHC-XfbB

Top 10 Scikit-learn (Python) Interview Questions – Senior Level (Global) If you are targeting advanced Python/Data Science roles, these Scikit-learn questions test deep understanding of machine learning pipelines, model evaluation, and real-world deployment challenges 1. How does Scikit-learn’s API design (fit, transform, predict) enable modular and reusable ML workflows? 2. What is the purpose of Pipelines in Scikit-learn, and how do they help prevent data leakage? 3. How do you choose between different algorithms (e.g., Random Forest, SVM, Logistic Regression) for a given problem? 4. Explain cross-validation strategies (k-fold, stratified, time-series split). When should each be used? 5. How do you handle imbalanced datasets using Scikit-learn techniques? 6. What are hyperparameter tuning methods (Grid Search, Random Search, Bayesian)? How do you optimize efficiently? 7. How do you evaluate model performance beyond accuracy (precision, recall, ROC-AUC, F1-score)? 8. How do you manage feature engineering and preprocessing (scaling, encoding, feature selection) in Scikit-learn? 9. How would you deploy a Scikit-learn model into production and monitor its performance over time? 10. When would you avoid Scikit-learn and use alternatives (TensorFlow, PyTorch, XGBoost)? Justify with scenarios. Follow: Akshay Kumawat akshay.9672@gmail.com 💬 Comment “Scikit Global” for answers 🌿 If you found this post valuable, please consider reposting to help others in your network