As artificial intelligence and machine learning continue to rule the globe as one of the most sought-after technologies, there is yet another arm of AI and ML that has been highly popular because of its salient characteristics – Deep learning.
Deep learning has been considered an ideal technology for complex problems faced by data scientists like finding out hidden patterns from huge data volumes and garnering a detailed understanding of relationships between different types of variables. It is most often used in industry-related applications like healthcare, image recognition, self-driving cars, stock analysis, fraud detection, news analysis, and many more.
Thanks to our global technology setup, there are many popular deep learning frameworks supported by big organizations and tech giants. Each of them has its own feature list and popularity quotient. But before we plunge into that list, let us understand what deep learning is.
“Artificial Intelligence, Deep Learning, Machine Learning – whatever you’re doing if you don’t understand it – learn it. Because otherwise, you’re going to be a dinosaur within 3 years.” – Mark Cuban
What Is Deep Learning?
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning – Wikipedia
Deep learning is a subset of the AI and ML family involving an artificial neural network with multiple layers that try to simulate human brain behavior and garner knowledge from the huge bulk of data. They are piled up in a hierarchy with the complexity of increasing value. Algorithms based on the deep learning framework use structured and unstructured data for its processing.
12 Deep Learning Frameworks That Are Popular
TensorFlow helps in building light models on mobile devices or embedded devices and it also helps in deploying heavy ML/DL models in huge production infrastructure. It is more like a symbolic Math library that depends upon differentiable programming and data flow. Amidst all programming languages, Python is the most preferred to work with TensorFlow apart from R, C++, etc.
It asks for a broad level of coding and operates with a static computation graph, along with its wrapper libraries. It is the best fit for creating deep learning models and architectures, with basic usage of different functions like SQL tables, inputting graphs, data integration, etc. It has a variety of tools like TensorBoard (visualization toolkit), TensorFlow Serving (rapid deployment of algorithms), etc.
- Effective graph visualization and queues
- Good community support and documentation
- Multiple GPU support
- Python-based and supported by Google
- Supports NLP, speech recognition, text classification, etc.
Majorly used for deep learning applications, PyTorch is a popular open-source library that is based on the Torch library. It is majorly meant for Python and hence the name. It is leveraged for applications like NLP, computer vision, etc. It includes activities ranging from research prototyping to production deployment.
The frontend acts as a fundamental ground for developing models and the backend offers distributed training and optimized performance. It makes use of a dynamically updated graph in which changes can be done to the architecture model while the training procedure is taking place.
PyTorch is considered apt to train, create and deploy smaller projects and prototypes. PyTorch has received a lot of appreciation amidst the deep learning community and is considered good for creating deep neural networks. Its architectural style is quite clear and uncomplicated.
- Good for rapid prototyping
- Easy to use and offers a clear interface
- Effective support for GPU
- Helps in data exchange with external libraries
- Simple scaling with pre-built images
As a well-known deep learning framework, Keras offers the best Python interface for artificial neural networks. It provides simple and reliable APIs meant for humans and not machines. It reduces the cognitive load and acts as an effective interface for the TensorFlow library. The best part about Keras is the speed at which it operates.
It comes with inbuilt support for data parallelism and can undergo the processing of huge data volumes with ease and speed. It is extensible and simple to operate as it is written in Python. It is mainly meant for high-level computations and not low-level. Keras is recommended for novices as it is easy to learn, and users can easily prototype basic concepts.
Keras helps in writing accurate and readable code. The network library in Keras supports recurrent and convolutional networks. It is light weighted, with an easily usable framework. The main use of Keras lies in speech recognition and translation, text generation, and summarization, classification, etc.
- Seamless integration with TensorFlow
- Good support for many deep learning backend technologies
- Inbuilt support for multi-GPU parallelism
- Reliable APIs with a high-level interface
- Supports most models of a neural network
Sonnet is deep learning, a high-level library that is designed to create complicated neural structures in TensorFlow. It is built on the top of TensorFlow and is responsible for developing and creating basic Python objects referring to a certain neural network. Later, these objects can be linked to the graph in TensorFlow.
Sonnet has a strong programming model that is created around a basic concept in which the modules are self-contained and decoupled from each other. It lets developers write modules that allow internal declaration of sub-modules or passing of the same to other modules. Users can also create their own modules.
The Sonnet library makes use of an object-oriented methodology, facilitating the creation of modules. Input and output tensors are used for calling modules. Automatic reuse of variables is done through sharing of variables. Sonnet encourages developers to write modules that can declare submodules in an internal manner or pass other modules.
- Offers simple abstractions for research
- Creates complicated neural network structures
- High level of abstraction for neural networks
- Enables creation of Python objects with ease
- Helps developers create their own modules
Powered by Apache, MXNet is a recognized deep learning software framework that is open source in nature and leveraged for training and deploying deep neural networks. It is an effective and flexible library that is the best fit for research prototyping and production. Being scalable in nature helps in encouraging fast model training.
MXNet has good support for many programming languages like Python, C++, R, Go, Scala, etc. It is portable in nature and scalable up to many GPUs and machines. Its lean and flexible property offers support for different deep learning models. There is strong support for multiple GPUs with optimization of computation.
It offers the flexibility to programmers to select their own programming style for the creation of deep learning models. Deep learning models like long short-term memory network (LSTM) and convolutional neural network (CNN) are facilitated by this framework. The major aim of this framework is to deliver high-end productivity and efficiency.
- Hybrid programming features
- Support for languages like Java, Python, Scala, R, C++, etc.
- Good scalability and distributed training
- Ideal for enterprises because of the variety of GPUs
- Capable for speech recognition, imaging, forecasting, NLP, etc.
Chainer is a recognized framework for neural networks that is flexible, powerful, and intuitive. As an open-source deep learning framework, it is one of the first ones to bring in the define-by-run methodology. Developers must define fixed connections within mathematical functions and then the actual computation analysis is done.
Chainer is considered apt for bridging the gap between the implementations of deep learning and the algorithms associated. It is a powerful extension library that can be utilized on many GPUs and offer great performance. It offers a flexible and insightful approach while implementation of neural networks, which proves to be highly beneficial.
Debugging is quite easy with Chainer since it uses the define-by-run approach. The training computation can be suspended with the inbuilt debugger and data can be examined. It is written completely in Python on the top of NumPy and CuPy Python libraries. It offers automatized differentiation APIs based on dynamic computational graphs.
- Supports CUDA/cuDNN computation using CuPy
- Works well with different network architectures
- Easy to debug and intuitive code
- Performance on large scale systems
- Offers acceleration using GPUs
Gluon is an open-source deep learning framework that helps in the easy and quick creation of machine learning models. It is a joint creation by AWS and Microsoft. It has a complete focus on increasing the speed, accessibility, and flexibility of machine learning technology for developers.
The entire process of the creation of deep learning models is highly simplified with Gluon without any sacrifice on speed and flexibility. Gluon has a brief API that helps in creating different neural network components. The coding involved is easy to understand, clear and transparent, making it easy for beginners to adapt.
Based on MXNet, Gluon is known for its flexible development approach making it a boon for developers. Yet, it does not compromise the performance quotient. Since it is a dynamic neural network, development can be done on the go with any structure that the developer chooses and with the help of Python’s features.
- Easily understandable code and versatile for novices
- Flexible and simple structure
- High performance
- Dynamic graphs
- Easy to prototype and experiment with
Deeplearning4j also known as DL4J is an open-source, distributed, deep learning library for the JVM. The J in DL4J stands for Java. It is developed in Java and has good support for other languages such as Kotlin, Scala, etc. The fundamental computations are in C, C++, and Cuda. DL4J makes maximum use of the popular distributed frameworks like Apache Spark, Hadoop, etc.
Deeplearning4j has open-source libraries that are maintained well by the developer community. It brings artificial intelligence to business frameworks on distributed GPUs and CPUs along with microservices architecture adaption. It comes with deep network support through different neural networks like DBN, CNN, RNN, etc.
It brings together the whole Java ecosystem to implement deep learning algorithms. Since it is majorly dependent on Java, it is must faster. DL4J has the competence to process huge amounts of data in a fast and effective manner, including single-threaded and multi-threaded deep learning.
- Flexible and fast results
- Powered by its library ND4J is unique and open source
- Easily administrable on top of Spark and Hadoop
- Easy to compose deep neural nets from shallow nets
- Training of neural networks through iterative reduction via clusters
Lasagne is a lightweight deep learning library that can be used to build and train neural networks in Theano. It is built on top of Theano, not hiding the Theano symbolic variables. It is easy to manipulate or modify the model in the way developers want to. It supports feed-forward networks such as LSTM, CNN, and many other combinations.
It facilitates the architectures of many inputs and outputs. It supports multiple optimization methods like ADAM, RMSprop, etc. Since it is based on Theano, due to the characteristic of symbolic differentiation, Lasagne has a freely definable cost function. There is no requirement of deriving gradients.
Lasagne offers great support to CPUs and GPUs owing to Theano’s expression compiler. Theano was a Python library that facilitates developers to define, optimize, and evaluate mathematical expressions including multi-dimensional arrays effectively. It is one of the most successful libraries built on top of Theano.
- Simple and easy to use
- Highly transparent and direct
- Modular way of architecture
- Clear and simplistic features and design
- Easy to manipulate Theano symbolic variables
The Open Neural Network Exchange (ONNX) is a well-known, deep learning framework, developed by the Linux Foundation. It is popular for the representation of machine learning algorithms in such a way that it encourages collaboration in the AI arena. ONNX has made it easy for developers to avail hardware optimizations with the help of ONNX-compatible runtime parameters.
ONNX based libraries are designed to garner the best performance from hardware components. Developers using ONNX can easily toggle between platforms. It possesses specialized functions like standard data types, inbuilt operators, computational graph models, etc. It offers libraries and runtimes that are easily compatible with the framework components.
ONNX offers converters for various ML frameworks like Keras, TensorFlow, etc. It is highly flexible and interoperates easily. It avoids framework lock-in by offering easy availability to optimized hardware and enabling model sharing. It also gives the flexibility to use the chosen framework with a relevant inference engine.
- Easy to avail hardware optimization
- Facilities choice of own framework with a related inference engine
- Availability of related connectors with ONNX models
- Comfortable switching between different machine learning frameworks
- Transfer of models from one framework to another
Caffe is a powerful deep learning framework developed at the University of Berkeley. Written in C++ with a Python interface, Caffe is known for its speed, expression, and modularity. It has good support for interfaces like C, C++, Command line, Python, etc. It has great applicability in creating CNN models.
The advantage of utilizing Caffe’s C++ library is availing the deep net repository that has networks that can be used instantly with Python support. The speed at which Caffe processes is commendable. It has the power to process millions of images routinely. It is considered ideal for vision recognition though its support for recurrent neural networks is not that strong.
Caffe is known to offer solutions for industrial applications in AI and academic research-based projects. This deep learning framework has a focus on machine vision, language, and multimedia. It offers extensive support for central processing units and graphics processing units.
- Powerful community support
- Fast and ideal for research experiments and deployment
- Extensibility in coding and hence active development
- Expressive architecture
- Easy to set up and learn
MATLAB is a high-performance language that is leveraged for a variety of deep learning, artificial intelligence, and machine learning projects. It has a range of interactive tools that perform different tasks including the import of data and connecting it to various sources. Applications can generate MATLAB code that can then facilitate in automation of tasks.
Many data scientists use MATLAB for a variety of applications like deep learning, signal processing, image processing, computational systems, video processing, control systems, etc. It has the competence to compute, visualize and program in a user-friendly atmosphere through mathematical notations.
It makes use of functions and tools to handle larger data sets. It uses limited lines of code to create models and deploy them across servers and embedded devices. It automatically generates inference engines from the cloud to different deployment infrastructures and garners a high-performance output.
- Supports object-oriented programming
- Huge library of mathematical functions
- Inbuilt graphics to better user experience
- Easy access to external data through disparate sources
On A Parting Note:
Choosing an ideal deep learning framework is a tough task. More so because there are so many of them and the list is increasing with the increase in demand for artificial intelligence development and machine learning applications.
One must think of different parameters to conclude – what types of neural networks are to be developed, what programming language is to be used, what is the budget, what is the main purpose of the project, what are the external tools and interfaces that will be needed and so on.
Good thought on these parameters and a detailed analysis of the above frameworks can help arrive at a conclusion on which deep learning framework would suit best!