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Deep Learning for Computer Vision with Python — ImageNet Bundle

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Inside this bundle, I demonstrate how to build a custom Python framework to train network architectures from scratch — this is the exact same framework I use when training my own neural networks. We'll use this framework to train AlexNet, VGGNet, SqueezeNet, GoogLeNet, and ResNet on the challenging ImageNet dataset. Using the training techniques I outline in this bundle, yo Inside this bundle, I demonstrate how to build a custom Python framework to train network architectures from scratch — this is the exact same framework I use when training my own neural networks. We'll use this framework to train AlexNet, VGGNet, SqueezeNet, GoogLeNet, and ResNet on the challenging ImageNet dataset. Using the training techniques I outline in this bundle, you'll be able to reproduce the results you see in popular deep learning papers and publications — this is an absolute must for anyone doing research and development in the deep learning space. To demonstrate advanced deep learning techniques in action, I provide a number of case studies, including age + gender recognition, emotion and facial expression recognition, car make + model recognition, and automatic image orientation correction. This bundle also includes a special BONUS GUIDE that reviews Faster R-CNNs and Single Shot Detectors (SSDs) and how to use them.


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Inside this bundle, I demonstrate how to build a custom Python framework to train network architectures from scratch — this is the exact same framework I use when training my own neural networks. We'll use this framework to train AlexNet, VGGNet, SqueezeNet, GoogLeNet, and ResNet on the challenging ImageNet dataset. Using the training techniques I outline in this bundle, yo Inside this bundle, I demonstrate how to build a custom Python framework to train network architectures from scratch — this is the exact same framework I use when training my own neural networks. We'll use this framework to train AlexNet, VGGNet, SqueezeNet, GoogLeNet, and ResNet on the challenging ImageNet dataset. Using the training techniques I outline in this bundle, you'll be able to reproduce the results you see in popular deep learning papers and publications — this is an absolute must for anyone doing research and development in the deep learning space. To demonstrate advanced deep learning techniques in action, I provide a number of case studies, including age + gender recognition, emotion and facial expression recognition, car make + model recognition, and automatic image orientation correction. This bundle also includes a special BONUS GUIDE that reviews Faster R-CNNs and Single Shot Detectors (SSDs) and how to use them.

48 review for Deep Learning for Computer Vision with Python — ImageNet Bundle

  1. 4 out of 5

    Alex A.

    Absolutely loved the book and the whole series, what an incredible journey it was. One of the major effects I captured while studying this last book, in particular, was that it builds up this intuitive understanding of a deep learning workflow while stressing the fact that it is an exploratory and sometimes tedious, error-prone, and time-consuming process which trains your patience, scientific thinking, childlike curiousity, and endurance. It though also empowers by providing the frameworks, the Absolutely loved the book and the whole series, what an incredible journey it was. One of the major effects I captured while studying this last book, in particular, was that it builds up this intuitive understanding of a deep learning workflow while stressing the fact that it is an exploratory and sometimes tedious, error-prone, and time-consuming process which trains your patience, scientific thinking, childlike curiousity, and endurance. It though also empowers by providing the frameworks, the tooling, practical examples, real-world stories and applications as well as follow-up ideas of approaching challenging tasks on large-scale datasets. Series like this will change the world, we need more of this level of thinking.

  2. 5 out of 5

    Chiểu Đỗ Văn

  3. 5 out of 5

    Said

  4. 5 out of 5

    Bảo Nguyễn Trần

  5. 5 out of 5

    Devendra

  6. 4 out of 5

    Suman Roy

  7. 4 out of 5

    Trọng Nguyễn

  8. 4 out of 5

    Parth1910gmail.Com

  9. 4 out of 5

    Ankur Haritosh

  10. 5 out of 5

    Iurie Cojocari

  11. 5 out of 5

    DY Wind

  12. 4 out of 5

    Ansam

  13. 5 out of 5

    Kamindra Kumar

  14. 4 out of 5

    Tresya Dompeipen

  15. 4 out of 5

    Bibek Bhusan

  16. 5 out of 5

    Nuwan

  17. 5 out of 5

    Sandeep Kumar G R

  18. 5 out of 5

    Madri De

  19. 4 out of 5

    Krishna Bhagavan

  20. 5 out of 5

    Sadeq

  21. 4 out of 5

    Ramya Chitti

  22. 5 out of 5

    Ramakrishna

  23. 5 out of 5

    Mohammad

  24. 5 out of 5

    Praful Sharma

  25. 4 out of 5

    Nikhil Gupta

  26. 4 out of 5

    Daniel

  27. 5 out of 5

    Rohit Srivastava

  28. 5 out of 5

    Ali Masri

  29. 4 out of 5

    Doğal Güzelsoy

  30. 4 out of 5

    Andrei Bazhenau

  31. 4 out of 5

    Önder Öztürk

  32. 4 out of 5

    Martin Power

  33. 4 out of 5

    Rene Thomsen

  34. 5 out of 5

    Rishabh

  35. 5 out of 5

    Pradeep

  36. 4 out of 5

    Paraskevi Christodoulou

  37. 4 out of 5

    Jawad Khan

  38. 4 out of 5

    Niteesh Kumar

  39. 5 out of 5

    Sarmad Gulzar

  40. 4 out of 5

    Ljubomir Polanc

  41. 5 out of 5

    Kimyoungyong

  42. 4 out of 5

    Nickolay Sokolov

  43. 5 out of 5

    Luciano Da Rosa

  44. 4 out of 5

    Alexander

  45. 4 out of 5

    f1yegor

  46. 4 out of 5

    Jonghwa Park

  47. 4 out of 5

    Sovit Rath

  48. 5 out of 5

    Metın

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