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.