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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Machine Learning

Get the Most out of LSTMs on Your Sequence Prediction Problem

Last Updated on August 14, 2019 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 will discover simple experiments you can run to ensure you are getting the most out of LSTMs on your […]

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Machine Learning

Multivariate Time Series Forecasting with LSTMs in Keras

Last Updated on August 28, 2020 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 develop an LSTM model for multivariate time series forecasting with the Keras deep learning library. After completing this tutorial, […]

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Machine Learning

Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

Last Updated on August 14, 2019 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 are designed specifically for sequence prediction problems. In this mini-course, you will discover how you can quickly bring LSTM […]

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Machine Learning

Stacked Long Short-Term Memory Networks

Last Updated on August 14, 2019 Gentle introduction to the Stacked LSTMwith example code in Python. The original LSTM model is comprised of a single hidden LSTM layer followed by a standard feedforward output layer. The Stacked LSTM is an extension to this model that has multiple hidden LSTM layers where each layer contains multiple memory cells. In this post, you will discover the Stacked LSTM model architecture. After completing this tutorial, you will know: The benefit of deep neural […]

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Machine Learning

CNN Long Short-Term Memory Networks

Last Updated on August 14, 2019 Gentle introduction to CNN LSTM recurrent neural networkswith example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. In this post, you will discover the CNN LSTM architecture for sequence prediction. After completing this post, you will know: […]

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Machine Learning

Encoder-Decoder Long Short-Term Memory Networks

Last Updated on August 14, 2019 Gentle introduction to the Encoder-Decoder LSTMs forsequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute programs are examples of seq2seq problems. In this post, you will discover the Encoder-Decoder LSTM architecture for sequence-to-sequence prediction. After […]

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Machine Learning

Gentle Introduction to Generative Long Short-Term Memory Networks

Last Updated on August 14, 2019 The Long Short-Term Memory recurrent neural network was developed for sequence prediction. In addition to sequence prediction problems. LSTMs can also be used as a generative model In this post, you will discover how LSTMs can be used as generative models. After completing this post, you will know: About generative models, with a focus on generative models for text called language modeling. Examples of applications where LSTM Generative models have been used. Examples of […]

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