What this book covers
Chapter 1, Deep Learning Overview, explores how deep learning has evolved.
Chapter 2, Algorithms for Machine Learning - Preparing for Deep Learning, implements machine learning algorithms related to deep learning.
Chapter 3, Deep Belief Nets and Stacked Denoising Autoencoders, dives into Deep Belief Nets and Stacked Denoising Autoencoders algorithms.
Chapter 4, Dropout and Convolutional Neural Networks, discovers more deep learning algorithms with Dropout and Convolutional Neural Networks.
Chapter 5, Exploring Java Deep Learning Libraries – DL4J, ND4J, and More, gains an insight into the deep learning library, DL4J, and its practical uses.
Chapter 6, Approaches to Practical Applications – Recurrent Neural Networks and More, lets you devise strategies to use deep learning algorithms and libraries in the real world.
Chapter 7, Other Important Deep Learning Libraries, explores deep learning further with Theano, TensorFlow, and Caffe.
Chapter 8, What's Next?, explores recent deep learning movements and events, and looks into useful deep learning resources.
