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Deep Learning for Computer Vision with Python #3
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, 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.
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.
321 pages, ebook
Published September 1, 2017
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Displaying 1 of 1 review
April 30, 2020
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.
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.
Displaying 1 of 1 review