# #deep-learning — MDRSS hashtag feed

> Public MDRSS cards tagged #deep-learning.
> Canonical feed: https://mdrss.com/feeds/deep-learning

## Cards (28)

### [Stable Diffusion web UI](https://mdrss.com/llm-engineering/models-and-training/2663/2663.md)

A web interface for Stable Diffusion, implemented using Gradio library. Detailed feature showcase with images: Make sure the required dependencies are met and follow the instructions available for: Alternatively, use online services (like Google Colab): 1.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [BS-RoFormer](https://mdrss.com/llm-engineering/models-and-training/2617/2617.md)

Implementation of Band Split Roformer , SOTA Attention network for music source separation out of ByteDance AI Labs. They beat the previous first place by a large margin.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Soundstorm - Pytorch](https://mdrss.com/llm-engineering/models-and-training/2613/2613.md)

Implementation of SoundStorm , Efficient Parallel Audio Generation from Google Deepmind, in Pytorch. They basically applied MaskGiT to the residual vector quantized codes from Soundstream .

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Coqui.ai News](https://mdrss.com/multimodal/speech-and-audio/2516/2516.md)

🐸TTS is a library for advanced Text-to-Speech generation. 🛠️ Tools for training new models and fine-tuning existing models in any language.

Classification: multimodal/speech-and-audio · Feed: multimodal · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Teaching Data Science](https://mdrss.com/llm-engineering/models-and-training/2408/2408.md)

Free, open-source Beamer slide decks and code for Machine Learning, Deep Learning, NLP, Generative AI, Maths for ML, and Python, from 1-hour seminars to full courses. Everything here, including slides, code, and notes, has been built by learning from, and citing, the best public material available, and is given back under an open license so anyone can teach, learn, or build on it.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Text Generation Inference](https://mdrss.com/llm-engineering/serving-and-retrieval/2140/2140.md)

A Rust, Python and gRPC server for text generation inference. Used in production at Hugging Face to power Hugging Chat, the Inference API and Inference Endpoints.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [rwkv.cpp](https://mdrss.com/llm-engineering/serving-and-retrieval/2075/2075.md)

This is a port of BlinkDL/RWKV-LM to ggerganov/ggml. Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [XTuring](https://mdrss.com/llm-engineering/models-and-training/1740/1740.md)

Fine‑tune, evaluate, and run private, personalized LLMs xTuring makes it simple, fast, and cost‑efficient to fine‑tune open‑source LLMs (e.g., GPT‑OSS, LLaMA/LLaMA 2, Qwen3, MiniMax M2, GPT‑J, GPT‑2, DistilGPT‑2, Mamba) on your own data — locally or in your private cloud. Why xTuring: Run a small, CPU‑friendly example first: Want bigger models and reasoning controls?

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Faster Whisper transcription with CTranslate2](https://mdrss.com/multimodal/speech-and-audio/1709/1709.md)

faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models. This implementation is up to 4 times faster than openai/whisper for the same accuracy while using less memory.

Classification: multimodal/speech-and-audio · Feed: multimodal · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [What is NannyML?](https://mdrss.com/llm-engineering/models-and-training/1663/1663.md)

Website • Docs • Community Slack NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and regression.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [GeoCalib is an algorithm for single-image calibration: it estimates the camera intrinsics and gravity direction from a single image only](https://mdrss.com/llm-engineering/models-and-training/1626/1626.md)

GeoCalib is an algorithm for single-image calibration: it estimates the camera intrinsics and gravity direction from a single image only. By combining geometric optimization with deep learning, GeoCalib provides a more flexible and accurate calibration compared to previous approaches.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [A Quick Overview](https://mdrss.com/llm-engineering/models-and-training/1583/1583.md)

MedSegDiff is a Diffusion Probabilistic Model (DPM) based framework for the Segmentation and Reconstruction of organs/tissues from the medical images. The algorithm is elaborated on our paper MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [MLX-LM-LORA](https://mdrss.com/llm-engineering/models-and-training/1483/1483.md)

With MLX-LM-LoRA you can, train Large Language Models locally on Apple Silicon using MLX. Training works with all models supported by MLX-LM, including: Training Types: Training Algorithms: Quantization Aware Training (QAT): Training Your Custom Preference Model: --- The main command is mlxlmlora.train.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Deep Learning Roadmap](https://mdrss.com/llm-engineering/models-and-training/1381/1381.md)

figure:: img/mainpage/subscribe.gif :target: https://machinelearningmindset.com/subscription/ Slack Group .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/astorfi/Deep-Learning-World/pulls ..

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [LazyLLM: A Low-code Development Tool For Building Multi-agent LLMs Applications](https://mdrss.com/ai-agents/agent-frameworks/1376/1376.md)

中文 | EN LazyLLM is a low-code development tool for building multi-agent large language model applications. It assists developers in creating complex AI applications at very low costs and enables continuous iterative optimization.

Classification: ai-agents/agent-frameworks · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Deep Learning Roadmap](https://mdrss.com/llm-engineering/models-and-training/1310/1310.md)

image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/osforscience/deep-learning-all-you-need/pulls .. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103 :target: https://github.com/ellerbrock/open-source-badge/ ..

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Deep Learning for Mathematical Reasoning (DL4MATH)](https://mdrss.com/llm-engineering/evaluation/1284/1284.md)

This repository is the reading list on Deep Learning for Mathematical Reasoning (DL4MATH). Contributors: Pan Lu @UCLA, Liang Qiu @UCLA, Wenhao Yu @Notre Dame, Sean Welleck @UW, Kai-Wei Chang @UCLA For more details, please refer to the paper: A Survey of Deep Learning for Mathematical Reasoning.

Classification: llm-engineering/evaluation · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [AI Audio Datasets (AI-ADS)](https://mdrss.com/llm-engineering/models-and-training/1276/1276.md)

AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [LoRA: Low-Rank Adaptation of Large Language Models](https://mdrss.com/llm-engineering/models-and-training/1260/1260.md)

This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. See our paper for a detailed description of LoRA.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Start Machine Learning in 2026 - Become an expert for free](https://mdrss.com/learning/curricula-and-careers/1238/1238.md)

This guide is intended for anyone having zero or a small background in programming, maths, and machine learning. There is no specific order to follow, but a classic path would be from top to bottom.

Classification: learning/curricula-and-careers · Feed: learning · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [What is Deep Lake?](https://mdrss.com/llm-engineering/models-and-training/1167/1167.md)

Deep Lake: Database for AI Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Storing and searching data plus vectors while building LLM applications 2.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Awesome Production Machine Learning](https://mdrss.com/llm-engineering/models-and-training/1055/1055.md)

This newer video covers the an updated 2024 version of the state of MLOps. You can join the Machine Learning Engineer newsletter.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Machine Learning for Trading — 3rd Edition](https://mdrss.com/crypto/funding-and-grants/1045/1045.md)

Build, test, and deploy ML-driven trading strategies — from data sourcing to live execution. This repository hosts the code for Machine Learning for Trading, 3rd Edition by Stefan Jansen — a ground-up rebuild, organized around one end-to-end workflow: how you define a research idea and develop it iteratively into a strategy you can actually run, and keep running, in a live market.

Classification: crypto/funding-and-grants · Feed: crypto · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Machine Learning From Scratch](https://mdrss.com/learning/curricula-and-careers/1044/1044.md)

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

Classification: learning/curricula-and-careers · Feed: learning · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Generating the documentation](https://mdrss.com/multimodal/vision-and-media/1039/1039.md)

To generate the documentation, you first have to build it. You don't have to commit the built documentation.

Classification: multimodal/vision-and-media · Feed: multimodal · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [NEW LIST 2023 - 2024: Machine-Learning / Deep-Learning / AI + Web3 -Tutorials](https://mdrss.com/llm-engineering/models-and-training/951/951.md)

I will be updating this tutorials site on a daily basis adding all relevant topcis for 2022 - 2024 especially pertaining to GPU programming, Data Centric AI, Emerging topics like Sustainable AI with Web3AI.js (DeFI, DAO, NFT) and much more. NOTE: All these tutorials are supported and accelerated on NVIDIA GPUs More importantly the applications of ML/DL/AI into industry areas such as Transportation, Medicine/Healthcare etc.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Awesome Geospatial](https://mdrss.com/llm-engineering/models-and-training/892/892.md)

Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. with GNSS (global navigation satellite system).

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Awesome Prompt Engineering](https://mdrss.com/ai-agents/prompting/866/866.md)

Awesome Prompt Engineering 🧙‍♂️ A hand-curated collection of resources for Prompt Engineering and Context Engineering — covering papers, tools, models, APIs, benchmarks, courses, and communities for working with Large Language Models. https://promptslab.github.io --- New to prompt engineering?

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1