The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models.

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TensorFlow is probably the gold standard for deep neural network development, although it is not without its defects. Two of the biggest issues with TensorFlow historically were that it was too hard to learn and that it took too much code to create a model. Both issues have been addressed over the last few years.

To make TensorFlow easier to learn, the TensorFlow team has produced more learning materials as well as clarifying the existing “getting started” tutorials. A number of third parties have produced their own tutorial materials (including InfoWorld). There are now multiple TensorFlow books in print, and several online TensorFlow courses. You can even follow the CS20 course at Stanford, TensorFlow for Deep Learning Research, which posts all the slides and lecture notes online.

There are several new sections of the TensorFlow library that offer interfaces that require less programming to create and train models. These include tf.keras, which provides a TensorFlow-only version of the otherwise engine-neutral Keras package, and tf.estimator, which provides a number of high-level facilities for working with models. These include both regressors and classifiers for linear, deep neural networks, and combined linear and deep neural networks, plus a base class from which you can build your own estimators. In addition, the Dataset API enables you to build complex input pipelines from simple, reusable pieces. You don’t have to choose just one. As this tutorial shows, you can usefully make tf.keras,, and tf.estimator work together.

TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices, which enables on-device machine learning inference (but not training) with low latency and a small binary size. TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API. TensorFlow Lite models are small enough to run on mobile devices, and can serve the offline use case.

The basic idea of TensorFlow Lite is that you train a full-blown TensorFlow model and convert it to the TensorFlow Lite model format. Then you can use the converted file in your mobile application on Android or iOS.

Alternatively, you can use one of the pre-trained TensorFlow Lite models for image classification or smart replies. Smart replies are contextually relevant messages that can be offered as response options; this essentially provides the same reply prediction functionality as found in Google’s Gmail clients.

Yet another option is to retrain an existing TensorFlow model against a new tagged dataset, an important technique called transfer learning, which reduces training times significantly. A hands-on tutorial on this process is called TensorFlow for Poets.

Cost: Free open source.

Platform: Ubuntu 14.04 or later, MacOS 10.11 or later, Windows 7 or later; Nvidia GPU and CUDA recommended. Most clouds now support TensorFlow with Nvidia GPUs. TensorFlow Lite runs trained models on Android and iOS.

Read my review of TensorFlow 2

Machine learning or deep learning?

Sometimes you know that you’ll need a deep neural network to solve a particular problem effectively, for example to classify images, recognize speech, or translate languages. Other times, you don’t know whether that’s necessary, for example to predict next month’s sales figures or to detect outliers in your data.

If you do need a deep neural network, then Keras, MXNet with Gluon, PyTorch, and TensorFlow with Keras or Estimators are all good choices. If you aren’t sure, then start with Scikit-learn or Spark MLlib and try all the relevant algorithms. If you get satisfactory results from the best model or an ensemble of several models, you can stop.

If you need better results, then try to perform transfer learning on a trained deep neural network. If you still don’t get what you need, then try building and training a deep neural network from scratch. To refine your model, try hyperparameter tuning.

No matter what method you use to train a model, remember that the model is only as good as the data you use for training. Remember to clean it, to standardize it, and to balance the sizes of your training classes.

At a Glance

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