wbj0110 / Deep-Learning-Book

Deep Learning book the covers the principles of deep learning, motivation, explanations, state of the art papers for the various tasks and architectures: CNNs, object detection, semantic segmentation, generative models, denoising, super resolution, style transfer and style manipulation, inpaintig, self supervised learning, vision transformers, OCR, and multi modal. Hope that it will be useful to some of you πŸ™‚

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Deep-Learning-Book

Deep Learning book the covers the principles of deep learning, motivation, explanations, state of the art papers for the various tasks and architectures:

  • Data Preprocessing
  • Weight Initialization
  • Activatation Functions
  • Loss functions
  • Optimization
  • Regularization
  • Convolutional Neural Netowrks
  • Object detection
  • Semantic Segmentation
  • Generative models
  • Denoising
  • Super resolution
  • Style transfer and style manipulation
  • Inpaintig
  • Self supervised learning
  • Vision Transformers
  • OCR
  • Multi modal
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Deep Learning book the covers the principles of deep learning, motivation, explanations, state of the art papers for the various tasks and architectures: CNNs, object detection, semantic segmentation, generative models, denoising, super resolution, style transfer and style manipulation, inpaintig, self supervised learning, vision transformers, OCR, and multi modal. Hope that it will be useful to some of you πŸ™‚