Volume 288 Proceedings of the International Conference on Neuro-symbolic Systems https://
proceedings.mlr.press/v288/ Is now available on PMLR
MACHINE LEARNING
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Volume 288: Neuro-symbolic Systems Conference Proceedings Published
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Live Deep Dive into SHAP, LIME, Grad-CAM and xai_evals
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🚨 We’re LIVE! Join us for a deep dive into SHAP, LIME, Grad-CAM & DL-Backtrace. Introducing xai_evals — the open-source benchmark for explainability methods. 🔴 Tune in here: hubs.la/Q03wsDFp0 #XAI #AIExplainability
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Stanford CS336: Comprehensive Course on Building Language Models from Scratch
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Stanford CS336: Language Models from Scratch! This is a comprehensive course on language models covers the full process of building one from scratch. It includes data collection, pretraining, transformer architecture, training, evaluation, and deployment.
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NVIDIA AI Aerial Neural Networks Boost Telecom Spectral Efficiency
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NVIDIA AI Aerial uses neural networks to learn from data, adapt in real time, and scale with complexity.
— NVIDIA AI (@NVIDIAAI) 9 juillet 2025
This boosts spectral efficiency, lowers costs, and improves connectivity for telecom.
To learn more, watch the full video ➡️ https://t.co/8CfgGk3nMq pic.twitter.com/WTUbBxDsPqNVIDIA AI Aerial uses neural networks to learn from data, adapt in real time, and scale with complexity. This boosts spectral efficiency, lowers costs, and improves connectivity for telecom. To learn more, watch the full video https://
nvda.ws/3TnSW1m -
MemAgent: Long-Context LLM Memory Architecture with RL
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MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent Yu et al.: https://
arxiv.org/abs/2507.02259 #ArtificialIntelligence #DeepLearning #MachineLearning -
XAI Methods Evaluation Session and xai_evals Framework Launch
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🔴 Just 2 Hours to Go! Join our closed-door session on evaluating XAI methods like SHAP, LIME, Grad-CAM & AryaXAI’s DL-Backtrace. Introducing xai_evals — an open-source framework to quantify & benchmark explainability tools. 🔗hubs.la/Q03wqGTZ0 #AIExplainability #XAI
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Deep Learning Curriculum Standard in Advanced AI Education
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pretty much every deep learning class includes a few weeks on it
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Reinforcement Learning: Neglected Tool in Language Models
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we all learn reinforcement learning in intro ML. it’s widely accepted as foundational, a pillar of the field yet outside of RLHF, everyone working on language models ignored RL for years folk wisdom said it “doesn’t work” what other fundamental tools are we neglecting?
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Most People Work on LLM Reinforcement Learning Today
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Think most people work on LLM RL to some extent these days.
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New AIoT Book Release: Hands-On AI for IoT Second Edition
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New #AIoT release from @PacktDataML >> Hands-On #AI for #IoT : Expert #MachineLearning and #DeepLearning techniques for developing smarter IoT systems [2nd Edition] See it at http://
amzn.to/3YAu6hS 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Leverage the power of Python libraries such as