--- id: "/non-llm-neural-networks-ml-foundations/advanced/page/4/" title: "Non-LLM neural networks & ML foundations for advanced learners · page 4" url: "https://skillissue.dev/non-llm-neural-networks-ml-foundations/advanced/page/4/" --- # Non-LLM neural networks & ML foundations for advanced learners [Beginner](/non-llm-neural-networks-ml-foundations/beginner/index.md) [Intermediate](/non-llm-neural-networks-ml-foundations/intermediate/index.md) [Advanced](/non-llm-neural-networks-ml-foundations/advanced/index.md) [Expert](/non-llm-neural-networks-ml-foundations/expert/index.md) 106 resources · page 4 of 4 - ### [A decoder-only foundation model for time-series forecasting (TimesFM)](/foundations/llm-fundamentals/timesfm-2/index.md) by [Abhimanyu Das et al. (Google)](https://ai.google/) arXiv paper (2023) introducing TimesFM, a decoder-only foundation model for time-series forecasting. Advanced–Expert learning_resource paper US en - ### [tslearn](/foundations/llm-fundamentals/tslearn/index.md) by [tslearn project](https://github.com/tslearn-team/tslearn) tslearn documentation: Python machine learning tools for time-series analysis. Advanced learning_resource docs France/global en - ### [U-Net](/foundations/llm-fundamentals/u-net/index.md) by [Olaf Ronneberger et al. (University of Freiburg)](https://www.uni-freiburg.de) arXiv paper (2015) "U-Net: Convolutional Networks for Biomedical Image Segmentation", introducing the encoder-decoder architecture with skip connections. Advanced–Expert learning_resource paper West en - ### [verifiers v1](/foundations/llm-fundamentals/verifiers-v1/index.md) by [Prime Intellect](https://primeintellect.ai/) Prime Intellect post on verifiers v1, splitting environments into tasksets, harnesses and runtimes for agentic RL and evals. Advanced learning_resource guide US en - ### [VGG](/foundations/llm-fundamentals/vgg/index.md) by [Karen Simonyan and Andrew Zisserman (University of Oxford)](https://www.ox.ac.uk/) arXiv PDF of the VGG paper (2014) on very deep convolutional networks for large-scale image recognition. Advanced–Expert learning_resource paper Europe — UK - ### [ViT](/foundations/llm-fundamentals/vit/index.md) by [Alexey Dosovitskiy et al. (Google)](https://ai.google/) arXiv paper (2020) introducing the Vision Transformer (ViT) for image recognition. Advanced–Expert learning_resource paper West en - ### [WaveNet](/foundations/llm-fundamentals/wavenet/index.md) by [Aaron van den Oord et al. (DeepMind)](https://deepmind.google/) arXiv paper (2016) "WaveNet: A Generative Model for Raw Audio", introducing WaveNet, an autoregressive generative model of raw audio. Advanced–Expert learning_resource paper West en - ### [Wide & Deep](/foundations/llm-fundamentals/wide-deep/index.md) by [Heng-Tze Cheng et al. (Google)](https://ai.google/) arXiv paper (2016) "Wide & Deep Learning for Recommender Systems", introducing Google's Wide & Deep recommender model. Advanced–Expert learning_resource paper West en - ### [xLSTM (NXAI, Linz)](/foundations/llm-fundamentals/xlstm-nxai-linz/index.md) by [Maximilian Beck et al. (NXAI)](https://www.nx-ai.com/) arXiv paper (2024) introducing xLSTM, extended long short-term memory with exponential gating and matrix memory. Advanced–Expert learning_resource paper West en - ### [YOLO](/foundations/llm-fundamentals/yolo/index.md) by [Joseph Redmon et al. (University of Washington)](https://www.washington.edu/) arXiv paper (2015) "You Only Look Once: Unified, Real-Time Object Detection", introducing YOLO, a single-stage real-time object detector. Advanced learning_resource paper West en [Previous page](/non-llm-neural-networks-ml-foundations/advanced/page/3/index.md) [Plain-text Markdown copy](/non-llm-neural-networks-ml-foundations/advanced/page/4/index.md)