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.
Deep Learning for Computer Vision with Python — ImageNet Bundle
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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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.
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