Machine Learning Career Guide
0.1.0
  • Coding
  • Machine Learning Theory
  • Machine Learning Systems
  • Applied Machine Learning
    • Canonical Micro-problems
    • Practical ML
    • Machine Learning Methods
      • Search & Recommendation
      • Multimodal Product Understanding
      • Machine Learning Tech
        • Language Understanding and Language Models
        • Large Language Models
        • Natural Language Processing
        • Vision Models
        • Graph Networks
        • Multimodal
      • 1. Supervised Learning
      • 2. Self-supervised & Weak Supervision
      • 3. Fine-tuning Strategies
      • 4. Labeling Techniques
      • 5. Training Tricks
      • 6. Representation & Retrieval
      • 7. Fusion Methods (for multimodal data)
      • 8. Domain Trade-offs
    • Problem Understanding
    • Practice Problems
    • End-to-end Design
  • Product System Design
  • General Skills
  • Non-Research Topics
Machine Learning Career Guide
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  • Applied Machine Learning »
  • Machine Learning Methods »
  • Machine Learning Tech »
  • Vision Models
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Vision Models¶

Resources¶

  • [lilianweng.github.io] Object Detection for Dummies

  • [arxiv.org] ResNet: Deep Residual Learning for Image Recognition

  • [arxiv.org] ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

  • [arxiv.org] SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

  • [arxiv.org] MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

  • [arxiv.org] YOLO: A Comprehensive Review of YOLO Architectures in Computer Vision

  • [arxiv.org] DETR: End-to-End Object Detection with Transformers

  • [arxiv.org] ImageGAN: Image-to-Image Translation with Conditional Adversarial Networks

Industry Resources¶

  • [] Visual Search at Pinterest

  • [] Learning a Unified Embedding for Visual Search at Pinterest

  • [] Unifying visual embeddings for visual search at Pinterest

  • [] SearchSage: Learning Search Query Representations at Pinterest

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