This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).

... reuse the same Disadvantage 1. Hyper-parameter tuning is non-trivial 2. Need a large database Recursive convolution ... weight sharing is limited 2. CNN is an NN which used convolution operation in place of simple matrix ...

... effectively prevent the weight from changing its value and can even com- pletely stop the neural network from further training . A solution is to use a long chain of short - term memory units , called long short - term memory units ( LSTMs ) ...

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems.

FPGA '17: The 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Feb 22, 2017-Feb 24, 2017 Monterey, USA.

However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only.

About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.

This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017.