Last Updated on August 14, 2019 Keras uses fast symbolic mathematical libraries as a backend, such as TensorFlow and Theano. A downside of using these libraries is that the shape and size of your data must be defined once up front and held constant regardless of whether you are training your network or making predictions. On sequence prediction problems, it may be desirable to use a large batch size when training the network and a batch size of 1 when […]

Last Updated on August 14, 2019 Long Short-Term Networks or LSTMs are a popular and powerful type of Recurrent Neural Network, or RNN. They can be quite difficult to configure and apply to arbitrary sequence prediction problems, even with well defined and “easy to use” interfaces like those provided in the Keras deep learning library in Python. One reason for this difficulty in Keras is the use of the TimeDistributed wrapper layer and the need for some LSTM layers to […]

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A common mistake made by beginners is to simply learn the mapping function from input term to the output term. […]

Last Updated on February 20, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This is a behavior required in complex problem domains like machine translation, speech recognition, and more. LSTMs are a complex area of deep learning. It can be hard to get your hands around what LSTMs are, and how terms like bidirectional and sequence-to-sequence relate to the field. In this post, you will get […]