Vocational training partnership with Siemens
We are your reliable partner for joint apprenticeship programs for your young talents. As a leading technology company with decades of experience, we provide customized vocational training and foundational qualifications at the highest technical level, ensuring your young talents are well-equipped for enduring success. Shaping the future together Benefit from the expertise of Siemens Professional […]
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30 Days of Building a Small Language Model — Day 1: Neural Networks
Welcome to day one. Before I introduce tokenizers, transformers, or training loops, we start where almost all modern machine learning starts: the neural network. Think of the first day as laying down the foundation you will reuse for the next twenty-nine days. If you have ever felt that neural networks sound like a black box, this post is for you. We will use a simple picture is this a dog or a cat? and walk through what actually happens inside the model, in plain language. What is a neural network? A neural network is made of layers. Each layer has many small units. Data flows in one direction: each unit takes numbers from the previous layer, updates them, and sends new numbers forward. During training, the network adjusts itself so its outputs get closer to the correct answers on example
Mean field sequence: an introduction
This is the first post in a planned series about mean field theory by Dmitry and Lauren (this post was generated by Dmitry with lots of input from Lauren, and was split into two parts, the second of which is written jointly). These posts are a combination of an explainer and some original research/ experiments. The goal of these posts is to explain an approach to understanding and interpreting model internals which we informally denote "mean field theory" or MFT. In the literature, the closest matching term is "adaptive mean field theory". We will use the term loosely to denote a rich emerging literature that applies many-body thermodynamic methods to neural net interpretability. It includes work on both Bayesian learning and dynamics (SGD), and work in wider "NNFT" (neural net field theor
AI Agents for Local Business: $500-1,500 Setup + Monthly Retainer
AI Agents for Local Business: $500-1,500 Setup + Monthly Retainer How to build ₹15K-₹75K/chatbot businesses serving Indian SMEs (no coding required) The Opportunity Nobody's Talking About While everyone's fighting over AI side hustles online, there's a goldmine happening offline: Local businesses desperately need AI—but have zero clue how to implement it. Real estate agents, dentists, gyms, restaurants, coaching centers—they're all losing customers because they can't respond to inquiries fast enough. You become the solution. The Business Model: Build AI chatbot once (8-15 hours) Charge setup fee: ₹15K-₹75K Charge monthly retainer: ₹3K-₹15K Maintain: 1-2 hours/month Profit margin: 70-85% Real Example: The Real Estate Bot Deal Client: Real estate agency in Bangalore (3 agents, 50+ properties
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Lenovo Brings Production-Scale AI to Global Sports: Enhancing Fan Experience, Driving Revenue Growth, Boosting Performance, and Improving Operational Efficiency with NVIDIA - Yahoo Finance
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