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 […]

Last Updated on August 27, 2020 Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post, you will discover the step-by-step life-cycle for creating, training, and evaluating Long Short-Term Memory (LSTM) Recurrent Neural Networks in Keras and how to make predictions with a trained model. After reading this post, you will know: How to define, compile, fit, and evaluate an LSTM in Keras. How […]

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) Recurrent Neural Networks are able to learn the order dependence in long sequence data. They are a fundamental technique used in a range of state-of-the-art results, such as image captioning and machine translation. They can also be difficult to understand, specifically how to frame a problem to get the most out of this type of network. In this tutorial, you will discover how to develop a simple LSTM recurrent neural […]

Last Updated on August 27, 2020 A powerful feature of Long Short-Term Memory (LSTM) recurrent neural networks is that they can remember observations over long sequence intervals. This can be demonstrated by contriving a simple sequence echo problem where the entire input sequence or partial contiguous blocks of the input sequence are echoed as an output sequence. Developing LSTM recurrent neural networks to address the sequence echo problem is both a good demonstration of the power of LSTMs and can […]