To stay competitive today, more companies are turning to artificial intelligence (AI) and machine learning to improve their services, offering predictive personalization and smart solutions to customers. But not all businesses have the technical prowess, capability, or manpower to create-or even understand-conventional AI and machine learning processes well enough to implement them.
Fortunately, some brilliant minds have created and generously open-sourced several deep learning frameworks that can be easily integrated by people with little to no knowledge of machine learning technology. Thanks to these frameworks, you can easily upload your data and train a deep learning model to perform accurate and intuitive predictive analysis for you.
So let's take a look at some of the best deep learning frameworks for 2019.
Created by the researchers at Google, TensorFlow is by far one of the most popular deep learning frameworks and has been adopted by the likes of Airbnb, Intel, and Twitter. It's been around since 2015, so it has an active and thriving community, constantly supplying support and updates.
It has the most GitHub activity and learning resources, making it relatively easy for people to learn. TensorFlow also happens to be the most used framework by developers right now, which means finding experienced deep learning developers to work on a project will be easier. Add to it the comprehensive documentation by Google and support for multiple languages, including C++, Python, and R, and you have a really comprehensive package for deep learning, leaving no reason why it wouldn't be a great option for you.
PyTorch is younger than TensorFlow but is racing ahead in popularity rather quickly. Coming from another powerhouse, Facebook, PyTorch has managed to garner massive interest in the developer community due to its flexibility and customizability. As of now, it is the second most popular standalone framework and could very well take over the top spot.
It builds on Torch, a scientific computing framework that is used to train and develop deep neural networks and perform tensor computations. It makes use of CUDA and C/C++ libraries to build robust yet flexible models scalable to each business. The PyTorch framework is built with a clean architectural style, making the process of training and developing deep learning models easy to learn and execute.
Keras is another deep learning framework that is seeing widespread and rapid adoption among developers, not just in the U.S. but on a global scale. One of the reasons behind its swift and worldwide popularity is its beginner-friendliness. It has a lightweight architecture, making it straightforward and easy to use. It runs on Python and supports recurrent as well as convolutional networks. This makes it ready to run on both TensorFlow as well as Theano.
In fact, the very reason Keras was built was to mitigate the challenges experienced while using TensorFlow, such as an intricate, low-level library and a slightly difficult interface. Keras can run on top of TensorFlow to provide a simpler interface. It requires very little code and is one of the easiest frameworks for beginners to work with.
Lauded for its speed, Caffe can process more than 60 million images every day with just a single NVIDIA K40 GPU. This makes it one of the most loved deep learning frameworks for image recognition. It works with C, C++, Python, MATLAB and even CLI. Its expressive architecture allows for training neural nets without hard coding and its extensible code encourages active development.
On tasks like simple regression, large-scale visual classification, speech and robotics applications, and even Siamese networks for image similarity, Caffe has adequate pre-trained networks and models that can get the job done easily. As a note, though, Caffe does seem to be a tad weak when it comes to language modeling and recurrent networks.
For all those who swear by Java, Deeplearning4j is the perfect deep learning framework. Since it is implemented in Java, it is, many believe, even more, efficient than Python. It lets you bring together the entire Java ecosystem and it can be administered over Hadoop or Spark, allowing the orchestration of multiple host threads. Also, since it uses MapReduce, it can train the neural networks even while depending on other libraries.
Some of the biggest strengths of Deeplearning4j are parallel training through iterative reduce, an industry-focused and distributed deep learning platform, and micro-service architecture. It works incredibly fast at image recognition when using multiple GPUs and has immense possibilities in text mining, fraud detection, natural language processing, and speech tagging.