Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]

# Author Archive | Jason Brownlee

## Multivariate Time Series Forecasting with LSTMs in Keras

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can […]

## Get the Most out of LSTMs on Your Sequence Prediction Problem

Long Short-Term Memory (LSTM) Recurrent Neural Networks are a powerful type of deep learning suited for sequence prediction problems. A possible concern when using LSTMs is if the added complexity of the model is improving the skill of your model or is in fact resulting in lower skill than simpler models. In this post, you […]

## How to Use Metrics for Deep Learning with Keras in Python

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if […]

## 10 Command Line Recipes for Deep Learning on Amazon Web Services

Running large deep learning processes on Amazon Web Services EC2 is a cheap and effective way to learn and develop models. For just a few dollars you can get access to tens of gigabytes of RAM, tens of CPU cores, and multiple GPUs. I highly recommend it. If you are new to EC2 or the […]

## How to Plan and Run Machine Learning Experiments Systematically

Machine learning experiments can take a long time. Hours, days, and even weeks in some cases. This gives you a lot of time to think and plan for additional experiments to perform. In addition, the average applied machine learning project may require tens to hundreds of discrete experiments in order to find a data preparation […]

## 9 Ways to Get Help with Deep Learning in Keras

Keras is a Python deep learning library that can use the efficient Theano or TensorFlow symbolic math libraries as a backend. Keras is so easy to use that you can develop your first Multilayer Perceptron, Convolutional Neural Network, or LSTM Recurrent Neural Network in minutes. You may have technical questions when you get started using […]

## How to Get Good Results Fast with Deep Learning for Time Series Forecasting

3 Strategies to Design Experiments and Manage Complexity on Your Predictive Modeling Problem. It is difficult to get started on a new time series forecasting project. Given years of data, it can take days or weeks to fit a deep learning model. How do you get started exactly? For some practitioners, this can lead to […]

## Why One-Hot Encode Data in Machine Learning?

Getting started in applied machine learning can be difficult, especially when working with real-world data. Often, machine learning tutorials will recommend or require that you prepare your data in specific ways before fitting a machine learning model. One good example is to use a one-hot encoding on categorical data. Why is a one-hot encoding required? […]

## What is the Difference Between a Parameter and a Hyperparameter?

It can be confusing when you get started in applied machine learning. There are so many terms to use and many of the terms may not be used consistently. This is especially true if you have come from another field of study that may use some of the same terms as machine learning, but they […]