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 […]
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. […]
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 […]
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 […]
The Long Short-Term Memory network or LSTM is a recurrent neural network that can learn and forecast long sequences. A benefit of LSTMs in addition to learning long sequences is that they can learn to make a one-shot multi-step forecast which may be useful for time series forecasting. A difficulty with LSTMs is that they […]
Long Short-Term Memory networks, or LSTMs, are a powerful type of recurrent neural network capable of learning long sequences of observations. A promise of LSTMs is that they may be effective at time series forecasting, although the method is known to be difficult to configure and use for these purposes. A key feature of LSTMs […]
Machine learning methods have a lot to offer for time series forecasting problems. A difficulty is that most methods are demonstrated on simple univariate time series forecasting problems. In this post, you will discover a suite of challenging time series forecasting problems. These are problems where classical linear statistical methods will not be sufficient and […]
Getting started in deep learning is a struggle. It’s a struggle because deep learning is taught by academics, for academics. If you’re a developer (or practitioner), you’re different. You want results. The way practitioners learn new technologies is by developing prototypes that deliver value quickly. This is a top-down approach to learning, but it is not the way […]
It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. […]
It can be difficult to get started in deep learning. Thankfully, a number of universities have opened up their deep learning course material for free, which can be a great jump-start when you are looking to better understand the foundations of deep learning. In this post you will discover the deep learning courses that you […]
