In 2014, an interesting contribution for image recognition was presented (for more information refer to: Very Deep Convolutional Networks for Large-Scale Image Recognition, by K. Simonyan and A. Zisserman, 2014). The paper shows that, a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. One model in the paper denoted as D or VGG-16 has 16 deep layers. An implementation in Java Caffe (http://caffe.berkeleyvision.org/) has been used for training the model on the ImageNet ILSVRC-2012 (http://image-net.org/challenges/LSVRC/2012/) dataset, which includes images of 1,000 classes and is split into three sets: training (1.3 million images), validation (50,000 images), and testing (100,000 images). Each image is (224 x 224) on three channels. The model achieves 7.5...
Deep Learning with Keras
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Deep Learning with Keras
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Overview of this book
This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided.
Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer.
Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (10 chapters)
Preface
Free Chapter
Neural Networks Foundations
Keras Installation and API
Deep Learning with ConvNets
Generative Adversarial Networks and WaveNet
Word Embeddings
Recurrent Neural Network — RNN
Additional Deep Learning Models
AI Game Playing
Conclusion
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