Machine Learning

📌 Why Every AI Coding Assistant Needs a Memory Layer 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-11 | ⏱️ Read time: 10 min read AI coding assistants need a persistent memory layer to overcome the statelessness of LLMs and… #DataScience #AI #Python

📌 Advanced RAG Retrieval: Cross-Encoders & Reranking 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-04-11 | ⏱️ Read time: 28 min read A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves… #DataScience #AI #Python

📌 When Things Get Weird with Custom Calendars in Tabular Models 🗂 Category: POWER BI 🕒 Date: 2026-04-10 | ⏱️ Read time: 10 min read Since September 2025, we have had Calendar-based Time Intelligence in Power BI and Fabric Tabular… #DataScience #AI #Python

📝 12 Essential Articles for Data Scientists 🏷 Article: Seq2Seq Learning with NN https://arxiv.org/pdf/1409.3215 An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning. 🏷 Article: GANs https://arxiv.org/pdf/1406.2661 An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence. 🏷 Article: Attention is All You Need https://arxiv.org/pdf/1706.03762 This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models. 🏷 Article: Deep Residual Learning https://arxiv.org/pdf/1512.03385 This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process. 🏷 Article: Batch Normalization https://arxiv.org/pdf/1502.03167 This paper introduced a technique that facilitates faster and more stable training of neural networks. 🏷 Article: Dropout https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf A straightforward method designed to prevent overfitting in neural networks. 🏷 Article: ImageNet Classification with DCNN https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf The first successful application of a deep neural network for image recognition. 🏷 Article: Support-Vector Machines https://link.springer.com/content/pdf/10.1007/BF00994018.pdf This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification. 🏷 Article: A Few Useful Things to Know About ML https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf A comprehensive collection of practical and empirical insights regarding machine learning. 🏷 Article: Gradient Boosting Machine https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM. 🏷 Article: Latent Dirichlet Allocation https://jmlr.org/papers/volume3/blei03a/blei03a.pdf This work introduced a model for text analysis capable of identifying the topics discussed within an article. 🏷 Article: Random Forests https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy. https://t.me/CodeProgrammer 🌟

📌 How Does AI Learn to See in 3D and Understand Space? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-10 | ⏱️ Read time: 19 min read How depth estimation, foundation segmentation, and geometric fusion are converging into spatial intelligence #DataScience #AI #Python

📌 A Guide to Voice Cloning on Voxtral with a Missing Encoder 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-10 | ⏱️ Read time: 13 min read Can we reconstruct audio codes if we have audio for the Voxtral text-to-speech model? #DataScience #AI #Python

📌 Why MLOps Retraining Schedules Fail — Models Don’t Forget, They Get Shocked 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-10 | ⏱️ Read time: 17 min read We fitted the Ebbinghaus forgetting curve to 555,000 real fraud transactions and got R² =… #DataScience #AI #Python

📌 The Future of AI for Sales Is Diverse and Distributed 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-09 | ⏱️ Read time: 11 min read True creativity and innovation will come from human-agent collaboration. One human, millions of agents. #DataScience #AI #Python

📌 A Survival Analysis Guide with Python: Using Time-To-Event Models to Forecast Customer Lifetime 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-09 | ⏱️ Read time: 13 min read Understand survival analysis by modeling customer retention through Kaplan-Meier curves and Cox Proportional Hazard regressions. #DataScience #AI #Python

📌 How Visual-Language-Action (VLA) Models Work 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-09 | ⏱️ Read time: 18 min read The mathematical foundations of Vision-Language-Action (VLA) models for humanoid robots and more #DataScience #AI #Python

📌 A Visual Explanation of Linear Regression 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-09 | ⏱️ Read time: 107 min read A long-form article featuring over 100 visualizations, covering a range of topics from how to… #DataScience #AI #Python

How a University Student Built a Game Changing Bot for Polymarket – And You Can Use It Too A computer science student built a bot that snipes trades before the market reacts! Meet Peter, who automated crypto trading by tracking blockchain data delays. He created the Oracle Lag Sniper to get in on Polymarket trades faster than anyone else. ⚡ Why it works: • Super Fast Execution: Snipes trades before the market catches up • Polymarket-Optimized: Built for speed & accuracy • Open Source & Free: Tweak it as you wish • Easy Setup: No tech skills required! Start using the Oracle Lag Sniper today. Head to GitHub, set it up, and make smarter, quicker trades. Sponsored by Polymarket Analytics

📌 Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-08 | ⏱️ Read time: 17 min read A clear mental model and a practical foundation you can build on #DataScience #AI #Python

✔️ 10 Books to Understand How Large Language Models Function (2026) 1. Deep Learning https://deeplearningbook.org The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts. 2. Artificial Intelligence: A Modern Approach https://aima.cs.berkeley.edu A fundamental perspective on artificial intelligence as a comprehensive system. 3. Speech and Language Processing https://web.stanford.edu/~jurafsky/slp3/ An in-depth examination of natural language processing, transformers, and linguistics. 4. Machine Learning: A Probabilistic Perspective https://probml.github.io/pml-book/ An exploration of probabilities, statistics, and the theoretical foundations of machine learning. 5. Understanding Deep Learning https://udlbook.github.io/udlbook/ A contemporary explanation of deep learning principles with strong intuitive insights. 6. Designing Machine Learning Systems https://oreilly.com/library/view/designing-machine-learning/9781098107956/ Strategies for deploying models into production environments. 7. Generative Deep Learning https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf Practical applications of generative models and transformer architectures. 8. Natural Language Processing with Transformers https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html Methodologies for constructing natural language processing systems based on transformers. 9. Machine Learning Engineering https://mlebook.com Principles of machine learning engineering and operational deployment. 10. The Hundred-Page Machine Learning Book https://themlbook.com A highly concentrated foundational overview without extraneous detail. 📚🤖

📌 How to Use Claude Code to Build a Minimum Viable Product 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-08 | ⏱️ Read time: 8 min read Learn how to effectively present product ideas by building MVPs with coding agents #DataScience #AI #Python

📌 Detecting Translation Hallucinations with Attention Misalignment 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-08 | ⏱️ Read time: 15 min read A low-budget way to get token-level uncertainty estimation for neural machine translations #DataScience #AI #Python

📌 Why AI Is Training on Its Own Garbage (and How to Fix It) 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-08 | ⏱️ Read time: 7 min read Deep Web Data Is the Gold We Can’t Touch, Yet #DataScience #AI #Python

📌 Democratizing Marketing Mix Models (MMM) with Open Source and Gen AI 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing… #DataScience #AI #Python

🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning Both code and weights are available under the MIT license on HuggingFace. 👉 Key details: • Trained from scratch (not a finetune) on proprietary data and infrastructure • Mixture-of-Experts (MoE) architecture Models: 🧠 GigaChat-3.1 Ultra • 702B MoE model for high-performance environments • Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks • Supports FP8 training and MTP ⚡️ GigaChat-3.1 Lightning • 10B model (1.8B active parameters) • Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks • Efficient local inference • Up to 256k context Engineering highlights: • Custom metric to detect and reduce generation loops • DPO training moved to native FP8 • Improvements in post-training pipeline • Identified and fixed a critical issue affecting evaluation quality 🌍 Trained on 14 languages (optimized for English and Russian) Use cases: • chatbots • AI assistants • copilots • internal ML systems Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.