RRepoGEO

REPOGEO REPORT · LITE

rasbt/scipy2023-deeplearning

Default branch main · commit 5114b654 · scanned 6/13/2026, 2:32:55 PM

GitHub: 599 stars · 101 forks

AI VISIBILITY SCORE
10 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface rasbt/scipy2023-deeplearning, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description and relevant topics to the repository metadata

    Why:

    CURRENT
    Description: (none)
Topics: (none)
    COPY-PASTE FIX
    Description: Official tutorial materials for the SciPy 2023 workshop on Modern Deep Learning with PyTorch, covering multi-GPU training and large language models.
Topics: scipy2023, deep-learning, pytorch, tutorial, workshop, multi-gpu, llm, transformers, machine-learning, python
  • highreadme#2
    Add a clear, concise purpose statement to the README's opening

    Why:

    CURRENT
    # SciPy 2023 Workshop

## Modern Deep Learning with PyTorch

At SciPy in Austin, Texas
    COPY-PASTE FIX
    # SciPy 2023 Workshop

## Modern Deep Learning with PyTorch

This repository contains the official tutorial materials for the SciPy 2023 workshop on modern deep learning with PyTorch.

At SciPy in Austin, Texas
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    Homepage: (none)
    COPY-PASTE FIX
    https://[official-scipy-2023-workshop-page-url]

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface rasbt/scipy2023-deeplearning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 5×
  2. tensorflow/tensorflow · recommended 3×
  3. fast.ai's Practical Deep Learning for Coders (v5) · recommended 1×
  4. fastai library · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How can I learn modern deep learning, including multi-GPU training and large language models?
    you: not recommended
    AI recommended (in order):
    1. fast.ai's Practical Deep Learning for Coders (v5)
    2. fastai library
    3. PyTorch
    4. Hugging Face Transformers Library
    5. Accelerate
    6. torch.nn.parallel.DistributedDataParallel
    7. torch.distributed package
    8. DeepLearning.AI's "Generative AI with Transformers"
    9. DeepLearning.AI's "Large Language Models (LLMs) Powered by Google Cloud"
    10. Google Cloud
    11. NVIDIA's Deep Learning Institute (DLI) Courses
    12. NVIDIA hardware
    13. "Dive into Deep Learning" (d2l.ai)
    14. TensorFlow
    15. JAX
    16. Papers with Code

    AI recommended 16 alternatives but never named rasbt/scipy2023-deeplearning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for training deep neural networks using multiple GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch DistributedDataParallel (DDP) (pytorch/pytorch)
    2. torch.nn.DataParallel (pytorch/pytorch)
    3. TensorFlow Distributed Strategy API (tensorflow/tensorflow)
    4. Horovod (horovod/horovod)
    5. NVIDIA NCCL (NVIDIA/nccl)
    6. NVIDIA Apex (NVIDIA/apex)
    7. PyTorch Automatic Mixed Precision (AMP) (pytorch/pytorch)
    8. TensorFlow Mixed Precision API (tensorflow/tensorflow)
    9. PyTorch `torch.nn.Module.to()` (pytorch/pytorch)
    10. DeepSpeed (microsoft/DeepSpeed)
    11. Megatron-LM (NVIDIA/Megatron-LM)
    12. PyTorch `torch.utils.data.DataLoader` (pytorch/pytorch)
    13. TensorFlow `tf.data` API (tensorflow/tensorflow)
    14. NVIDIA Nsight Systems
    15. TensorBoard (tensorflow/tensorboard)

    AI recommended 15 alternatives but never named rasbt/scipy2023-deeplearning. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of rasbt/scipy2023-deeplearning?
    pass
    AI did not name rasbt/scipy2023-deeplearning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts rasbt/scipy2023-deeplearning in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name rasbt/scipy2023-deeplearning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo rasbt/scipy2023-deeplearning solve, and who is the primary audience?
    pass
    AI did not name rasbt/scipy2023-deeplearning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite