Deep Learning, NLP, NMT, AI, ML
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 […]
Read more
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 […]
Read more
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 […]
Read more
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 […]
Read more
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 […]
Read more
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 […]
Read more
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 […]
Read more
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. […]
Read more
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 […]
Read more
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, […]
Read more