Temporär
  • Deep Learning Tuning Playbook (link)

CNN

  • Convolutional Neural Networks (CNNs / ConvNets) (link)
  • 1×1 Convolution In Detail (link)
  • Comprehensive look at 1X1 Convolution (link)
  • 6 Significant Computer Vision Problems Solved by ML (link)
  • A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way (link)
  • How to Train Your ResNet (link)
  • Convolution arithmetic tutorial (link)
  • Transposed Convolutions explained with… MS Excel! (link)
  • Up-sampling with Transposed Convolution (link)
  • CNNs from different viewpoints (link)

Enhancing Photos With Deep Learning

Image Segmentation / Object Detection

  • Mediapipe (link)
  • ActionDetectionforSignLanguage (link)
  • UNet (link)
  • SemTorch - Architectures definitions for image segmentation (link)
  • DE:TR: End-to-End Object Detection with Transformers (link)
  • Review: YOLOv3 — You Only Look Once (Object Detection) (link)
  • R-CNN (Object Detection) (link)
  • Review of Deep Learning Algorithms for Object Detection (link)
  • Image Segmentation in 2021: Architectures, Losses, Datasets, and Frameworks (link)
  • Review of Deep Learning Algorithms for Image Semantic Segmentation (link)
  • R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms (link)
  • YOLO (v3) object detector from scratch in PyTorch (link)
  • Image Processing with Python — Unsupervised Learning for Image Segmentation (link)
  • Fastai - Video (link)
  • FastFCN git (link)

Misc

  • Document Denoising Using Deep Learning (link)
  • Few shot learning — learning to learn from a few examples (link)
  • Paper Explained- Vision Transformers (link)
  • Why gradient descent doesn’t converge with unscaled features? (link)
  • Generalization in Neural Network (link)
  • Sarcasm detection in news headlines — on cAInvas (link)
  • Contrastive Representation Learning (link)
  • CLIP git (link)
  • DALL·E: Creating Images from Text (link)
  • Image Data Labelling and Annotation — Everything you need to know (link)
  • Activation Functions — All You Need To Know! (link)
  • Loss Functions Explained (link)
  • Deep Dive into Math Behind Deep Networks (link)
  • Everything you need to know about “Activation Functions” in Deep learning models (link)
  • Weight Initialization Techniques in Neural Networks (link) (link)
  • Understanding Variational Autoencoders (VAEs) (link)
  • Table Detection, Information Extraction and Structuring using Deep Learning (link)
  • Deconvolution and Checkerboard Artifacts (link)
  • Troubleshooting Deep Neural Networks (link)

Optimization Methods

  • Vectorizing Gradient Descent — Multivariate Linear Regression and Python implementation (link)
  • The LookAhead optimizer (link)
  • New Deep Learning Optimizer, Ranger: Synergistic combination of RAdam + LookAhead for the best of both. (link)
  • Understanding Optimizers (link) “Adam” and friends (link)
  • Adam — latest trends in deep learning optimization. (link)
  • Various Optimization Algorithms For Training Neural Network (link)
  • Which Optimizer should I use for my Machine Learning Project? (link)
  • Gradient Descent (link)
  • Gradient Descent Algorithm — a deep dive (link)
  • Intro Momentum, RMSProp and Adam (link)
  • An overview of gradient descent optimization algorithms (link)
  • BatchNorm 1 (link)
  • BatchNorm 2 (link)
  • BatchNorm 3 (link)
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