--- id: "/non-llm-neural-networks-ml-foundations/page/5/" title: "Non-LLM neural networks & ML foundations · page 5" url: "https://skillissue.dev/non-llm-neural-networks-ml-foundations/page/5/" --- # Non-LLM neural networks & ML foundations Non-LLM neural networks & ML foundations - [classical ML & statistics](/non-llm-neural-networks-ml-foundations/classical-ml-statistics/index.md) (8) - [generative theory](/non-llm-neural-networks-ml-foundations/generative-theory/index.md) (1) - [graph NNs](/non-llm-neural-networks-ml-foundations/graph-nns/index.md) (8) - [recommenders](/non-llm-neural-networks-ml-foundations/recommenders/index.md) (10) - [reinforcement learning](/non-llm-neural-networks-ml-foundations/reinforcement-learning/index.md) (41) - [sequence models](/non-llm-neural-networks-ml-foundations/sequence-models/index.md) (16) - [time series & tabular](/non-llm-neural-networks-ml-foundations/time-series-tabular/index.md) (10) - [vision](/non-llm-neural-networks-ml-foundations/vision/index.md) (44) [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) 138 resources · page 5 of 5 - ### [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 - ### [EASA AI Concept Paper Proposed Issue 03](/foundations/llm-fundamentals/easa-ai-concept-paper-proposed-issue-03/index.md) by [European Union Aviation Safety Agency (EASA)](https://www.easa.europa.eu/) EASA AI Concept Paper Proposed Issue 03 (PDF), extending aviation AI assurance guidance to more methods and Level 3 automation. Expert learning_resource guide European Union en - ### [Reward Hacking in Reinforcement Learning (Lil'Log)](/foundations/llm-fundamentals/lilian-weng-reward-hacking-in-rl/index.md) by [Lilian](https://lilianweng.github.io) Lilian Weng's survey of reward hacking in reinforcement learning, including in RLHF and coding tasks. Expert learning_resource guide US en - ### [Mamba](/foundations/llm-fundamentals/mamba/index.md) by [Albert Gu and Tri Dao (Carnegie Mellon University)](https://www.cmu.edu/) arXiv paper (2023) introducing Mamba, linear-time sequence modelling with selective state spaces. Expert learning_resource paper West en [Previous page](/non-llm-neural-networks-ml-foundations/page/4/index.md) [Plain-text Markdown copy](/non-llm-neural-networks-ml-foundations/page/5/index.md)