shellysheynin/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 🙂
This is a comprehensive book that breaks down the core principles of deep learning, from foundational concepts like data preprocessing and optimization to advanced architectures. It explains how to build and understand models for tasks such as object detection, image segmentation, and generating new images. This resource is perfect for anyone looking to understand how deep learning works and apply it to computer vision and related problems.
108 stars. No commits in the last 6 months.
Use this if you are a student, researcher, or practitioner who wants to learn the theoretical underpinnings and practical applications of deep learning, particularly in computer vision.
Not ideal if you are looking for a quick-start guide focused solely on implementing pre-built deep learning models without understanding their internal mechanisms.
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