How to Evaluate the Skill of Deep Learning Models

Last Updated on August 14, 2020 I often see practitioners expressing confusion about how to evaluate a deep learning model. This is often obvious from questions like: What random seed should I use? Do I need a random seed? Why don’t I get the same results on subsequent runs? In this post, you will discover the procedure that you can use to evaluate deep learning models and the rationale for using it. You will also discover useful related statistics that […]

September 29, 2020 Machine Learning

How to Report Classifier Performance with Confidence Intervals

Last Updated on August 14, 2020 Once you choose a machine learning algorithm for your classification problem, you need to report the performance of the model to stakeholders. This is important so that you can set the expectations for the model on new data. A common mistake is to report the classification accuracy of the model alone. In this post, you will discover how to calculate confidence intervals on the performance of your model to provide a calibrated and robust […]

September 29, 2020 Machine Learning

How to Calculate Bootstrap Confidence Intervals For Machine Learning Results in Python

Last Updated on August 14, 2020 It is important to both present the expected skill of a machine learning model a well as confidence intervals for that model skill. Confidence intervals provide a range of model skills and a likelihood that the model skill will fall between the ranges when making predictions on new data. For example, a 95% likelihood of classification accuracy between 70% and 75%. A robust way to calculate confidence intervals for machine learning algorithms is to […]

September 29, 2020 Machine Learning

The 5 Step Life-Cycle for Long Short-Term Memory Models in Keras

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 […]

September 29, 2020 Machine Learning

How to Learn to Echo Random Integers with LSTMs in Keras

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 […]

September 29, 2020 Machine Learning

How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers

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 […]

September 29, 2020 Machine Learning

How to Get Reproducible Results with Keras

Last Updated on August 19, 2019 Neural network algorithms are stochastic. This means they make use of randomness, such as initializing to random weights, and in turn the same network trained on the same data can produce different results. This can be confusing to beginners as the algorithm appears unstable, and in fact they are by design. The random initialization allows the network to learn a good approximation for the function being learned. Nevertheless, there are times when you need […]

September 29, 2020 Machine Learning

How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras

Last Updated on August 27, 2020 Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning […]

September 29, 2020 Machine Learning

Data Preparation for Variable Length Input Sequences

Last Updated on August 14, 2019 Deep learning libraries assume a vectorized representation of your data. In the case of variable length sequence prediction problems, this requires that your data be transformed such that each sequence has the same length. This vectorization allows code to efficiently perform the matrix operations in batch for your chosen deep learning algorithms. In this tutorial, you will discover techniques that you can use to prepare your variable length sequence data for sequence prediction problems […]

September 29, 2020 Machine Learning

How to Handle Missing Timesteps in Sequence Prediction Problems with Python

Last Updated on August 28, 2020 It is common to have missing observations from sequence data. Data may be corrupt or unavailable, but it is also possible that your data has variable length sequences by definition. Those sequences with fewer timesteps may be considered to have missing values. In this tutorial, you will discover how you can handle data with missing values for sequence prediction problems in Python with the Keras deep learning library. After completing this tutorial, you will […]

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