Search Results for "deep learning with python" - Page 71

This project develops a game for practicing mental calculus. Its purpose is to help children learning to calculate rapidly and to help grown ups to learn new tricks (e.g., Trachtenberg's method) to get better at mental arithmetics.

The learnbt is an open source learning bt protocol and c++ written and usable in C++. It is linux & unix platform software.

The Local Content Server transforms Plone into a robust Learning Object Repository. It supports IMS content package transport, content preview, and object identification and registration.

Match is a calculator for checking marriage or friendship compatibility. Muhurtha is used for finding good timing and using the Life Predictor you can make predictions up to 120 years. Explore each calculator to get a deep understanding of Vedic Astrology. Anybody who has studied Vedic Astrology knows well how accurate it can be. But also how complex it can get to make accurate predictions. It takes decades of experience to be able make accurate prediction. As such this knowledge only...

...The model is efficient for both cloud inference with vLLM and local deployment using llama.cpp or Ollama, thanks to its bf16 precision and AMP training. While the base model is not fine-tuned for downstream tasks, it is designed to be easily adapted through supervised fine-tuning (SFT) or reinforcement learning (RL). Benchmarks on RepoBench, SAFIM, and HumanEval demonstrate its competitive performance, with specialized fine-tuned versions for Python already showing strong improvements.

Unstructured text is no match for Litersta - see further details here: https://litersta.com Working with text now becomes effortless when paired with Litersta textual analytics software. Unlike database fields, which are easily queried, text contains unstructured data that must be parsed for key objects that can be transformed in to powerful metrics. Litersta - textual analytics - software leverages statistical algorithms to programmatically locate, and extract, overall document...

DeepSeek-V3.2 is a cutting-edge large language model developed by DeepSeek-AI, focused on achieving high reasoning accuracy and computational efficiency for agentic tasks. It introduces DeepSeek Sparse Attention (DSA), a new attention mechanism that dramatically reduces computational overhead while maintaining strong long-context performance. Built with a scalable reinforcement learning framework, it reaches near-GPT-5 levels of reasoning and outperforms comparable models like DeepSeek-V3.1...