A mobile app with lots of features and attractive UI/UX design has become a thing of the past. With the arrival of disruptive advancements (voice recognition, behavior analysis, and more) and rise in users’ demands, app/software development companies are leaving no stone upturned to develop hi-tech mobile and web solutions.
When we talk about the latest technology, terms like deep learning, machine learning, artificial intelligence, big data, and more come to mind. These technologies have made software and apps to perform various tasks with great accuracy and at a speed that no human can beat. Here, we are talking about one of the promising technology, deep learning.
Deep learning frameworks, when used with artificial intelligence, can amplify the efficiency and productivity of businesses. AI/ML developers working on deep learning use Python, Java, and C++ language to build applications and software. Besides this, we are listing down the top five deep learning frameworks that most AI and ML development companies use. Check them out below:
Top 5 Most Popular Deep Learning Frameworks
- TensorFlow
- Keras
- Caffe
- PyTorch
- Sonnet
Let’s read about them in detail:
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TensorFlow
TensorFlow is undoubtedly the most popular deep learning framework that is being used by Airbnb, Gmail, Uber, and many other popular firms. To work with TF, using Python is the most suitable client language. The framework considers highly powerful and effective computing clusters. Moreover, it can also run models on Android and iOS platforms.
Those who have worked with TF are aware of this fact that this framework requires a lot of coding. It means you cannot develop a powerful AI model overnight. TensorFlow works with a static computation graph, which means you need to mention the graph first to make calculations, retrain the AI model, or modify the architecture. The framework is a suitable choice for developing cross-platform solutions.
PyTorch
PyTorch is the second name in the list of top most popular deep learning frameworks. This was mainly created for Facebook services; however, companies like Twitter and Salesforce are also using it. The library of PyTorch works with a dynamically updated graph. It simply means you can change the architecture without any hassle.
Not just this, PyTorch also allows the developers to use standard debuggers to ensure flawless creation of the AI/ML models. Using PyTorch, developers can easily create and train a neural network as the framework already includes many trained models. This deep learning framework is mainly preferred for small projects and prototyping.
Keras
Keras is a perfect choice for those who want to deal with an enormous amount of data to create AI and deep learning models. This deep learning framework offers a highly minimalist approach for accessing TensorFlow. You can use this framework as a high-level application programming interface for other libraries. For creating deep learning giant models using this top rated framework, you need to use single-line functions.
The framework is a well-written API that facilitates you to access lower-level frameworks. Keras is a suitable deep learning framework for understanding and prototyping basic concepts. The framework delivers clear, concise, and readable codes. As compared to TensorFlow, Keras is on a higher level.
Caffe
Caffe is one of the most popular deep learning frameworks that are known for its speed. Interfaces such as Command Line, C, Python, C++, Python, and MATLAB are compatible with this framework. Caffe can process millions of images in one day using a singleNvidia K40 GPU. Suitable for vision recognition, the framework doesn’t support extremely fine network layers as that of TensorFlow. Caffe is simpler to set up and train.
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Sonnet
This deep learning framework is dedicatedly designed to make neural networks with complex architecture. The main purpose of Sonnet is to develop the basic Python objects associated with a particular part of the neural network.
The developed objects are separately linked to the computational graph of the TensorFlow framework. Sonnet is best for recreating the research defined in its developer’s documentation without any hassle.
Besides the five top frameworks mentioned in this article, some of the other popular deep learning frameworks are Swift, Chainer, MXNet, ONNX, Gluon, and DL4j. To know which among these frameworks will be best for your project, reach out to a trusted AI/ML development company with your specific app or software development requirements.
Wrapping Up
The article talks about the most efficient deep learning frameworks that the ML and AI development companies are using to build highly advanced software and applications. Most Popular Frameworks such as TensorFlow, Keras, Caffe, PyTorch, and Sonnet help in building deep learning models quickly and efficiently. Moreover, they also relieve the developers from the hassle of looking into the details of an algo. To know more about how to build an app using these frameworks, reach out to an AI app development company.