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  • Scientists Hijacked Nvidia CUDA for Deep Learning Research

    By

    @ylecun

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    Nvidia did not "sell its gaming architecture to a handful of scientists" A handful of scientists such as Patrice Simard et MSR, Andrew Ng, and later Geoff Hinton, hijacked CUDA and implemented convnets and backprop on them. It took several years for Nvidia to realize there was

    → View original post on X — @ylecun

  • Linear Algebra for Artificial Intelligence and Machine Learning
    Linear Algebra for Artificial Intelligence and Machine Learning

    By

    @gp_pulipaka

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    Linear Algebra for Artificial Intelligence! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/LA-for-Al-Intelligen…

    → View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC

  • Tacotron: TensorFlow Implementation for Machine Learning
    Tacotron: TensorFlow Implementation for Machine Learning

    By

    @gp_pulipaka

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    Tacotron: Tensorflow Implementation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Tecotron [Translated from EN to English]

    → View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC

  • Bayesian Neural Networks: Big Data and Machine Learning Guide
    Bayesian Neural Networks: Big Data and Machine Learning Guide

    By

    @gp_pulipaka

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    Bayesian Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Bayesian-Neural-Nets

    → View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC

  • Top Python Github Repositories to Learn Data Science and AI
    Top Python Github Repositories to Learn Data Science and AI

    By

    @gp_pulipaka

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    Top Python Github Repositories to Learn! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Top-Python-GitHub-Re…

    → View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC

  • Training Neural Networks with Optical Backpropagation Technology
    Training Neural Networks with Optical Backpropagation Technology

    By

    @gp_pulipaka

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    Training Neural Network on Optical Backpropagation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Linnainmaa, S. I. (1976). Taylor expansion of the accumulated rounding error. BIT Numerical Mathematics, 16(2), 146–160. Published June 1976. Retrieved March 10, 2025, from doi.org/10.1007/BF01931367 Lvovsky, A. I., Guo, X., & Spall, J. (2025). Training neural networks with end-to-end optical backpropagation. Advanced Photonics, 7(1), 016004. Published February 4, 2025. Retrieved March 10, 2025, from doi.org/10.1117/1.AP.7.1.016… Nielsen, M. A. (2015). How the backpropagation algorithm works. In Neural networks and deep learning. Published 2015. Retrieved March 10, 2025, from neuralnetworksanddeeplearnin… Spall, J., Guo, X., & Lvovsky, A. I. (2025). The optical implementation of backpropagation (Oxford, Lumai). Semiconductor Engineering. Published February 4, 2025. Retrieved March 10, 2025, from semiengineering.com/the-opti…

    → View original post on X — @gp_pulipaka, 2026-04-12 14:26 UTC

  • InfoTok: Efficient Long Video Processing Using Information Theory
    InfoTok: Efficient Long Video Processing Using Information Theory

    By

    @jiqizhixin

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    How do we process long videos efficiently without losing crucial information? NVIDIA, Stanford University, and National University of Singapore have an answer! They introduce InfoTok, a breakthrough method inspired by Shannon's information theory. It intelligently allocates

    → View original post on X — @jiqizhixin

  • Goal-VLA: Zero-Shot Robot Manipulation from Images and Instructions
    Goal-VLA: Zero-Shot Robot Manipulation from Images and Instructions

    By

    @jiqizhixin

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    What if robots could perform complex manipulation tasks with zero prior examples, just from an image and instruction? Researchers from National University of Singapore, The University of Hong Kong, Peking University, and Tsinghua University present Goal-VLA! Their Goal-VLA uses

    → View original post on X — @jiqizhixin

  • OpenClaw-RL Repository and Free AI/ML Engineering PDF Guide

    By

    @akshay_pachaar

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    OpenClaw-RL Repo: github.com/Gen-Verse/OpenCla… If you want to learn AI/ML engineering, I have put together a free PDF (380+ pages) with 150+ core lessons. Download for free: dailydoseofds.github.io/ai-e…

    → View original post on X — @akshay_pachaar, 2026-04-12 13:35 UTC

  • OpenClaw-RL: Reinforcement Learning for Agent Model Weights
    OpenClaw-RL: Reinforcement Learning for Agent Model Weights

    By

    @akshay_pachaar

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    OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: – Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. – On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

    → View original post on X — @akshay_pachaar, 2026-04-12 13:35 UTC