In the modern world, an increasing number of businesses are using machine learning and artificial intelligence (AI) to streamline their operations and stay one step ahead of the competition.
Organizations are now able to offer their clients intelligent solutions and predictive personalization thanks to the development of machine learning and artificial intelligence. However, not all businesses can use machine learning and AI in their processes due to a variety of factors.
The assistance of various deep learning frameworks is useful in this situation. People with little to no experience using machine learning and artificial intelligence can quickly incorporate these interfaces, libraries, or tools because they are typically open-source. You can submit data and train a deep learning model with the aid of deep learning frameworks, producing accurate and understandable predictive insights.
What is a Deep Learning Framework?
A deep learning framework is an interface, library, or tool that makes it easier and faster to create Deep Learning Modelsby removing the need to understand the specifics of the underlying algorithms. Utilizing a selection of already constructed and optimized components, they offer a straightforward and concise method for defining models.
We can utilize an appropriate framework to develop such a model rapidly rather than writing hundreds of lines of code. Below are some of the critical features of an excellent deep learning framework:
Optimized for performance
Easy to understand and coded
Good community support
Parallelize processes to reduce computation
Automatically calculate transitions
Here, In this blog, we look at some of the top 8 deep learning frameworks (in no particular order) to give you a better idea of which framework will be a perfect fit or fit for your business problems.
1. TensorFlow
Google created and unveiled TensorFlow, a machine learning and deep learning framework, in 2015. The most broadly used deep learning framework today is TensorFlow, which is often utilized by data scientists and AI specialists as well as by well-known companies like Google, NVIDIA, and Uber.
TensorFlow is a library for Python, although it is being ported to other popular languages such as Java, JavaScript, C++, and more. However, building a deep learning model with TensorFlow requires a lot of resources because it depends on using a lot of coding to define the network’s structure.
A commonly cited disadvantage of TensorFlow is that it works with a static computational graph, meaning that the algorithm must be executed every time to see changes. However, the platform itself is highly extensible and powerful, contributing to its high adoption level.
2. PyTorch
PyTorch is TensorFlow’s primary rival in the deep learning framework industry in many aspects. PyTorch is an open-source deep learning framework for Python that was designed and developed by Facebook. The majority of Facebook’s services are powered by other businesses including Johnson & Johnson, Twitter, and Salesforce.
As its name suggests, PyTorch offers built-in support for Python and even allows users to use standard debuggers provided by the software. Unlike TensorFlow’s static graph, PyTorch has a dynamic computation graph that lets users quickly see how their changes will affect the final result when programming a solution.
The framework offers more straightforward options for training neural networks using modern technologies such as data parallelism and distributed learning. The community for PyTorch is also very active, and pre-trained models are published regularly. However, TensorFlow beats PyTorch in providing a cross-platform solution because Google’s vertical integration with Android offers TF users more power.
Keras is a deep learning framework built on top of other prominent frameworks such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). Although it loses out to PyTorch and TensorFlow in terms of programmability, it is an ideal starting point for beginners to learn neural networks.
Keras enables users to build huge and intricate models with straightforward commands. Because of this, it is less configurable than its rivals, but making prototypes and proofs-of-concept is significantly easier. The software is available in any situation thanks to its application programming interface (API) accessibility.
4. MXNet
MXNet is an open-source deep learning framework created by the Apache Software Foundation with high scalability and multi-language support. Multiple languages, including C++,Python, Java, Scala, Julia, R, Javascript, Perl, Go, and Wolfram, are used to create MXNet. It is known for its fast model training. Compatible with Windows, macOS, and Linux. It can run on smart devices because it is lightweight and memory efficient. Its hybrid nature through distributed training allows MXNet to combine symbolic and imperative operations, making it efficient, flexible, and portable. Its active community is the most effective community with synchronized ideas on AI and deep learning.
API – Gluon Python API, support from Scala, Julia, R, Java, Perl, Clojure
Application – AWS (as DL framework), Warner Bros. Entertainment Group of Companies, Elbit Systems of America, Kwai
5. Chain
Developed by Preferred Networks in collaboration with Intel, Nvidia, IBM, and Microsoft. Based on the Numpy and CuPy libraries, Chain is a cross-platform deep learning framework that is entirely written in Python. Chainer became popular as a result of its dynamic computational graphs, which are simple to use via API. The term “define-on-run access” refers to this function. Chainer features four additional libraries: ChainerUI, ChainerRL for reinforcement learning, ChainerCV for computer vision, and ChainerMN for increased GPU usage (for management and visualization).
API – Python
Apps PaintsChainer (auto-painting), JPMorgan Chase, Novu LLC, Facebook ADP, Mad Street Den.
6. Caffe
Caffe, a program created by the University of California, Berkeley, offers a number of picture segmentation and classification designs. In C++, Caffe is written. Compatible with Windows, macOS, and Linux. Works on the CPU but has better performance with GPU acceleration. Caffe is preferred for its speed and industrial deployment. With an NVIDIA GPU, it can process up to 60 million images. It has proper documentation and an active developer community to support prototypes for startups and research work.
API – Python and Matlab
Applications CaffeOnSpark (a Yahoo venture with Apache Spark integration), Caffe2 (by Facebook), Snap Inc., Cadence Design Systems, Qualcomm
7. Flux
The only machine learning library is written in Julia. Flux supports CNN, RNN, and LSTM deep learning architectures. The change keeps building a pipeline to integrate Flux models. It has extensions like DiffEqFlux.jl for neural differential equations via differential programming and Zygote.jl for automatic differentiation.
API – Julia
Application – used in homomorphically encrypted data (encryption technique that does not require decryption of the data)
8. Matlab Deep Learning Toolbox
Developed by MathWorks, MATLAB deep learning Toolbox is written in C, C++, Java, and MATLAB. It can run on operating systems such as Linux, Windows, and macOS. This framework supports all kinds of deep learning architectures CNN, RNN, LSTM, GAN, and pre-built models like ResNets. The toolkit provides GPU parallel training. The Deep Designer application monitors the parameters used.
Conclusion
Deep learning’s introduction has sparked a variety of real-world applications for Machine Learningand AI in general. Deep understanding probably made it possible to divide tasks in the simplest way to help machines as efficiently as possible.
