Showing 3 open source projects for "reason"

View related business solutions
  • $300 Free Credits for Your Google Cloud Projects Icon
    $300 Free Credits for Your Google Cloud Projects

    Start building on Google Cloud with $300 in free credits. No commitment, no credit card required until you're ready to scale.

    Launch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
    Start Free Trial
  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
    Try It Free
  • 1
    Deep Learning Is Nothing

    Deep Learning Is Nothing

    Deep learning concepts in an approachable style

    ...Practical sections cover data pipelines, regularization, and evaluation, emphasizing reproducibility and debugging techniques. The goal is to replace buzzwords with intuition so learners can reason about architectures and training dynamics with confidence.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    Deep-Learning-Interview-Book

    Deep-Learning-Interview-Book

    Interview guide for machine learning, mathematics, and deep learning

    ...It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions rather than memorize trivia. Many entries connect theory to implementation details, including how choices in activation, initialization, or normalization affect convergence and stability. The content is organized for fast review before an interview loop but is also deep enough for systematic study over weeks. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    End-to-End Negotiator

    End-to-End Negotiator

    Deal or No Deal? End-to-End Learning for Negotiation Dialogues

    ...The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations over shared resources. The framework provides code for both supervised learning (training from human dialogue data) and reinforcement learning (via self-play and rollout-based planning). It introduces a hierarchical latent model, where high-level intents are first clustered and then translated into coherent language, improving dialogue diversity and goal consistency. ...
    Downloads: 6 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
Auth0 Logo