Deep learning frameworks
TensorFlow is mathematical software, and an open source software library for machine intelligence. The Google Brain team developed it in 2011 and open-sourced it in 2015. The main features offered by the latest release of TensorFlow (v1.8 during the writing of this book) are faster computing, flexibility, portability, easy debugging, a unified API, transparent use of GPU computing, easy use, and extensibility. Once you have constructed your neural network model, after the necessary feature engineering, you can simply perform the training interactively using plotting or TensorBoard.
Keras is a deep learning library that sits atop TensorFlow and Theano, providing an intuitive API inspired by Torch. It is perhaps the best Python API in existence. DeepLearning4J relies on Keras as its Python API and imports models from Keras and through Keras from Theano and TensorFlow.
Theano is also a deep learning framework written in Python. It allows using GPU, which is 24x faster than a single CPU. Defining, optimizing, and evaluating complex mathematical expressions is very straightforward in Theano.
Neon is a Python-based deep learning framework developed by Nirvana. Neon has a syntax similar to Theano's high-level framework (for example, Keras). Currently, Neon is considered the fastest tool for GPU-based implementation, especially for CNNs. But its CPU-based implementation is relatively worse than most other libraries.
PyTorch is a vast ecosystem for ML that offers a large number of algorithms and functions, including for DL and for processing various types of multimedia data, with a particular focus on parallel computing. Torch is a highly portable framework supported on various platforms, including Windows, macOS, Linux, and Android.
Caffe, developed primarily by Berkeley Vision and Learning Center (BVLC), is a framework designed to stand out because of its expression, speed, and modularity.
MXNet (http://mxnet.io/) is a deep learning framework that supports many languages, such as R, Python, C++, and Julia. This is helpful because if you know any of these languages, you will not need to step out of your comfort zone at all to train your deep learning models. Its backend is written in C++ and CUDA and it is able to manage its own memory in a way similar to Theano.
The Microsoft Cognitive Toolkit (CNTK) is a unified deep learning toolkit from Microsoft Research that makes it easy to train and combine popular model types across multiple GPUs and servers. CNTK implements highly efficient CNN and RNN training for speech, image, and text data. It supports cuDNN v5.1 for GPU acceleration.
DeepLearning4J is one of the first commercial-grade, open source, distributed deep learning libraries written for Java and Scala. This also provides integrated support for Hadoop and Spark. DeepLearning4 is designed to be used in business environments on distributed GPUs and CPUs.
DeepLearning4J aims to be cutting-edge and plug-and-play, with more convention than configuration, which allows for fast prototyping for non-researchers. The following libraries can be integrated with DeepLearning4 and will make your JVM experience easier whether you are developing your ML application in Java or Scala.
ND4J is just like NumPy for JVM. It comes with some basic operations of linear algebra such as matrix creation, addition, and multiplication. ND4S, on the other hand, is a scientific computing library for linear algebra and matrix manipulation. It supports n-dimensional arrays for JVM-based languages.
To conclude, the following figure shows the last 1 year's Google trends concerning the popularity of different DL frameworks:
The trends of different DL frameworks. TensorFlow and Keras are most dominating. Theano is losing its popularity. On the other hand, DeepLearning4J is emerging for JVM.