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Top Deep Learning Frameworks

Deep learning, a vital element of data science, is a class of machine learning that works similarly to the human brain. Simply, it can be considered a proxy for predictive modeling, a statistical technique to predict future states. Many of the top deep learning frameworks make this process quicker and more straightforward, making it advantageous to data scientists who gather, analyze, and interpret massive amounts of data. 

Deep learning frameworks differ from traditional ML frameworks in linearity. While traditional ones are linear, deep learning frameworks have stacked algorithms in complex hierarchies (or layers). Each algorithm in the hierarchy performs a nonlinear transformation on its input and outputs a statistical model using what it has learned. Iterations keep pushing until the output is accurate enough. Thus, the word “deep” justifies the presence of multiple processing layers that the data must go through. 

Convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and deep belief networks (DBN) are some popular deep learning frameworks that are commonly used for many commercial applications and use cases like NLP, computer vision, etc. This article enlists some of the top deep learning frameworks.

Top deep learning frameworks

  1. Sonnet

Sonnet is an advanced framework for deep learning developed by Google’s DeepMind for TensorFlow, its a fundamental framework due to its versatility and adaptability for creating even higher-level frameworks. Sonnet does not replace TensorFlow for DL-based tasks but makes it easier for the users to construct neural networks. Before it, developers had to be proficient with all underlying TensorFlow graphs, but now all they have to do is construct Python objects and connect them separately to form a TensorFlow computation graph. 

It features multiple pre-defined modules snt.Linear, snt.Conv2D, snt.BatchNorm, etc. Users can also write customized modules and submodules to create models. These models can be readily integrated using raw TF codes or codes written in other advanced languages. 

  1. Eclipse Deeplearning4j

Deeplearning4j is a comprehensive suite of tools for deep learning. The framework is highly compatible with JVM and allows you to train models from java while incorporating the Python ecosystem. Its model support, cpython bindings, and interoperability between multiple runtimes like onnxruntime.

Deeplearning4j comes with numerous submodules like samediff (for complex graphs), Nd4j (numpy ++ for java), Libnd4j (C++ library), Python4j (for easy deployment of python scripts), and others. You may either use Deeplearning4j as an addition to your current Python and C++ workflows or as a standalone library to create and deploy models.

Several renowned enterprises, the US Bank, Nuix, and Teladoc Health are among hundreds of others that use Deeplearning4j.

  1. TensorFlow

TensorFlow is an open-source framework developed by the Google Brain team for internal research and production. The E2E platform allows developers to build and deploy machine learning models with built-in neural network executions. The framework offers scalability by training across multiple resources simultaneously and seamless support for transitioning from shared memory to distributed memory. 

TensorFlow supports a variety of programming languages. TensorFlow is most frequently in Python to ensure stability. There is also support for other languages, including JavaScipt, C++, and Java. Programming language flexibility enables a broader choice of industrial applications.

Enterprises like Airbnb, eBay, AirBus, DropBox, Snapchat, and others actively utilize TensorFlow for text-based services, image recognition, voice search, etc.

  1. PyTorch

PyTorch is an open-source and one of the top deep learning frameworks based on Torch, a package containing data structures for multi-dimensional tensors. The framework, developed by Facebook’s AI Research Lab, accelerates the process from prototyping to production deployment by focusing on computer vision and NLP tasks. 

PyTorch is similar to NumPy in computations, but instead of arrays, it uses n-dimensional arrays or tensors. It provides a massive GPU-powered computation speed, an autograd package for gradient computation, a set of modules for neural network functionalities, and many more features. 

AMD, Intel, Pfizer, NVIDIA, Intuitive Surgical, and many other established enterprises use PyTorch for CRM, marketing, campaign management, and other applications.

  1. Caffe2

Caffe, created by Berkeley AI Research (BAIR), is one of the top deep learning frameworks that promote modularity, performance, and expression. Caffe enables automation, image processing, statistical analysis, and other tasks when dealing with massive data sets. Meta research has developed Caffe2 to be an advanced version of the previous framework in terms of flexibility, robustness, and scalability to make deep learning more straightforward and uncomplicated. 

Caffe2 is compatible with native Python and C++ APIs that are interchangeable, allowing for rapid prototyping and simple optimization. It can also integrate with Android Studio, XCode, or Microsoft Visual Studio for mobile development.

Many organizations like Snap Inc, Qualcomm, Meridian Health, Thermo Fisher Scientific, and others use Caffe2 for its deep learning capabilities. 

  1. Kaldi

Kaldi is an open-source deep learning toolkit explicitly designed for speech recognition based on C++. The framework is highly flexible and efficient in training acoustic models (statistical representations of acoustic information). Kaldi compiles the OpenFst toolkit to modernize coding and provide several “recipes” for model training. 

The framework has bindings for Python, MATLAB, Java, and other programming languages. Many companies like Microsoft, Google, IBM, Apple, and others use Kaldi under the Apache 2.0 license.

  1. Theano

Theano is a Python-based library built on NumPy that enables you to define, optimize, and evaluate mathematical expressions in deep learning with multi-dimensional arrays. Named after “Theano,” a greek mathematician, the library was introduced by the Montreal Institute for Learning Algorithms in 2007. It features a transparent use of GPU for data-intensive computations, avoids nasty bugs in complex computations, supports dynamic C code generation, and tools for detecting potential problems. 

Theano functions in three stages: first, it defines the objects or variables; next, it progresses through phases to determine the mathematical expressions; and finally, it evaluates expressions by receiving input values.

Companies like Vizual.ai, Vuclip, Zetaops, and others actively use Theano in their technology stacks. 

  1. Scikit-learn 

Next on our list of top deep learning frameworks is Scikit-learn, a Python-based toolkit, that was unveiled in 2007 during the Google Summer of Code project hosted by David Cournapeau. It was designed to operate and facilitate machine learning and artificial intelligence algorithms and work in conjunction with several frameworks like NumPy, SciPy, Matplotlib, Pandas, etc.

Other than supporting deep learning, it is used for statistical modeling with regression, classification (K-nearest neighbors), clustering (K-means and K-means++), preprocessing, and dimensionality reduction. 

The toolkit is being actively used by organizations like Spotify, Inria, and J.P. Morgan to enhance their linear algebra and statistical analysis.

  1. Apache MXNet

Apache MXNet is an open-source, efficient, and versatile library for deep learning. The library features hybrid front-end support and seamless transitions from other frameworks. MXNet can be extended with Apache’s thriving ecosystem of tools and libraries to enable more real-world use cases like NLP, computer vision, etc. It also features scalable and distributed training and performance optimization with its dual Parameter Server support. 

MXNet is compatible with 8 integrable language bindings like C++, Java, Julia, Python, Perl, R, Go, etc. You can follow Apache’s guide to build and install MXNet from the official website. 

Many companies like Intel, Amazon, Baidu, Carnegie, Wolfram Research, and others use and contribute to the community, yet it remains limited. 

  1. Microsoft Cognitive Toolkit

Microsoft’s cognitive toolkit (CNTK) is an open-source toolkit for distributed deep learning that describes neural networks as a sequence of computations based on a directed graph. With CNTK, users can conjugate and work with multiple models like CNNs (convolutional neural networks), DNNs (deep neural networks), LSTM (long short-term memory), and RNNs (recurrent neural networks). 

CNTK is accessible as a library in Python, C++, or C# programming languages and can also be used as a standalone tool with its language (BrainScript). The toolkit supports 64-bit Windows or Linux operating systems. You can install the pre-compiled binary package or compile the toolkit by sourcing from GitHub.  

Many famous companies like Delta Air, General Electric, Bain & Company, and many others use CNTK for personalized analytics.

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Disha Chopra
Disha Chopra
Disha Chopra is a content enthusiast! She is an Economics graduate pursuing her PG in the same field along with Data Sciences. Disha enjoys the ever-demanding world of content and the flexibility that comes with it. She can be found listening to music or simply asleep when not working!

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