Machine Learning

5 Examples of Simple Sequence Prediction Problems for LSTMs

Last Updated on August 14, 2019 Sequence prediction is different from traditional classification and regression problems. It requires that you take the order of observations into account and that you use models like Long Short-Term Memory (LSTM) recurrent neural networks that have memory and that can learn any temporal dependence between observations. It is critical to apply LSTMs to learn how to use them on sequence prediction problems, and for that, you need a suite of well-defined problems that allow […]

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Machine Learning

A Gentle Introduction to Mini-Batch Gradient Descent and How to Configure Batch Size

Last Updated on August 19, 2019 Stochastic gradient descent is the dominant method used to train deep learning models. There are three main variants of gradient descent and it can be confusing which one to use. In this post, you will discover the one type of gradient descent you should use in general and how to configure it. After completing this post, you will know: What gradient descent is and how it works from a high level. What batch, stochastic, […]

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Machine Learning

How Much Training Data is Required for Machine Learning?

Last Updated on May 23, 2019 The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm. This is a fact, but does not help you if you are at the pointy end of a machine learning project. A common question I get asked is: How much data do I need? I cannot answer this question directly for you, or for anyone. But I can give you a handful […]

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Machine Learning

What is the Difference Between a Parameter and a Hyperparameter?

Last Updated on June 17, 2019 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 are used differently. For example: the terms “model parameter” and “model hyperparameter.” Not having a clear definition for these […]

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Machine Learning

Why One-Hot Encode Data in Machine Learning?

Last Updated on June 30, 2020 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? Why can’t you fit a model on your data directly? In this post, you will discover the answer to […]

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Machine Learning

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

Last Updated on August 5, 2019 3 Strategies to Design Experiments and Manage Complexity onYour 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 paralysis and even procrastination at the very beginning of a project. In others, it can result in being caught in […]

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Machine Learning

9 Ways to Get Help with Deep Learning in Keras

Last Updated on August 19, 2019 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 Keras. You may need a little help. In this post, you will discover 9 places where you can ask […]

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Machine Learning

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 model and model configuration that gives good or great performance. The drawn-out nature of the experiments means that you need to carefully plan and manage […]

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Machine Learning

10 Command Line Recipes for Deep Learning on Amazon Web Services

Last Updated on August 19, 2019 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 Linux command line, there are a suite of commands that you will find invaluable when running your deep learning […]

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Machine Learning

How to Use Metrics for Deep Learning with Keras in Python

Last Updated on August 27, 2020 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 you want to keep track of a performance measure that better captures the skill of your model during training. […]

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