The right tools for a job can make all the difference, which is certainly the case in the tech space. Many developers refer to deep learning frameworks as building blocks because they simplify the training, validating, and designing stages.
This blog looks at the top nine deep learning frameworks, showcasing their advantages and key features, and how Coding Dojo is the answer to learning data science and entering the field.
What Is a Deep Learning Framework?
A deep learning framework is a software bundle that allows developers and data scientists to quickly design and train new deep learning models without diving into the specifics of the underlying algorithms. Ultimately, deep learning frameworks provide clear guidelines for defining models with some help from libraries full of pre-built and already optimized components.
Why Use Deep Learning Frameworks?
Deep learning frameworks give developers a cheat sheet to train their new models without building a brand-new model. All frameworks function like a digital library, containing information ranging from defining layers to network types (CNNs, RNNs) and typical model architectures. These frameworks let developers save time and avoid the headaches of starting over.
9 Popular Deep Learning Frameworks
Overall, deep learning frameworks provide developers with the foundation to create effective models that can massively simplify some of the most complex problems. Below are the top nine most popular deep learning frameworks. Read on to learn more about each of their features and advantages.
- TensorFlow is capable of running on both iOS and Android operating systems.
- Using the power of graphs, TensorFlow allows developers and data scientists to display large data sets rapidly and efficiently.
- With tons of flexibility, TensorFlow allows programmers to use this deep learning framework on any compatible device.
- As an open-source deep learning framework, TensorFlow is accessible and free to use, making it one of the most favorable framework options.
- TensorFlow is one of the most effective deep learning frameworks capable of manipulating data structures and arranging them by rank, type, and shape.
PyTorch is another open-source deep-learning framework created by Facebook. This framework uses the Torch library for its foundation, with the primary objective of speeding up the timeline from research to releasing a finalized product to market. While TensorFlow holds the top power score of 96.77, PyTorch comes in second with a power score of 51.55, showcasing the power of this framework.
- PyTorch uses the C++ programming language, which sits on top of a Python interface, making it an adaptable framework.
- When debugging any faulty code, developers can use standard debuggers such as PDB or PyCharm.
- Any graphs used by PyTorch are updated automatically, making it easy to make changes to the overall model structure.
- PyTorch uses the Python programming language, making it one of the most straightforward frameworks to learn due to its intuitive structure pattern.
- With lots of documentation, PyTorch is one of the most highly organized and valuable deep learning frameworks.
- Perfect for small projects, thanks to its ability to build and deploy prototypes quickly, PyTorch is an excellent framework for tight timelines.
Keras is another open-source deep learning framework. Compared to many other frameworks, Keras is unique in that it can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML. Keras comes with pre-built-in support for data parallelism. This feature gives Keras the power to sift through massive amounts of raw data, resulting in more efficient training periods for new models.
- Keras provides developers with real-time feedback, making it easy to fix defective code, leading to shorter project timelines.
- With a user-friendly API, Keras lets developers quickly create prototypes for brand-new deep-learning models.
- The Keras framework comes with pre-trained weights, making it possible for data scientists to make faster, more accurate predictions.
- As another framework that runs off Python, Keras is beginner-friendly and easy to learn.
- Keras has some of the most detailed documentation available for any deep learning framework, with tons of examples and materials, making this option perfect for beginners.
- As one of the top deep learning frameworks, Keras can assist programmers in developing truly cutting-edge models and optimized metrics.
Chainer is another open-source deep learning framework using the Python programming language and the NumPy and CuPy libraries. This framework is the first to introduce a define-by-run approach to determine the fixed connections between mathematical problems within the network. Of all deep learning frameworks, Chainer carries the lowest power score of only 1.18 out of 100.
- Chainer is capable of being used on multiple GPUs at the same time, resulting in an overall faster performance.
- Chainer has not one but four libraries, ChainerMN, ChainerRL, ChainerCV, and ChainerUI, stocked full of pre-built components.
- As one of only a few frameworks supporting forward computation, the Chainer framework can manage flow statements of Python without needing backpropagation.
- As one of the most powerful frameworks, Chainer can support CUDA computation, enabling only a few lines of code to leverage a graphics processing unit (GPU).
- The Chainer framework can work with many network architectures, such as feed-forward nets, convnets, recurrent nets, recursive nets, and per-batch architectures.
- With the Chainer framework, forward computation is possible, which helps to produce code that’s easier to read and debug.
Gluon is an open-source deep-learning framework that allows developers to make models efficiently. This relatively new framework provides a straightforward API to define deep learning models using a selection of pre-built and optimized neural network components. Gluon also comes with an assortment of plug-and-play neural network building blocks, such as predefined layers, optimizers, and initializers.
- Gluon copies much of the MXNet framework, allowing it to produce clean APIs resulting in massively simplified deep learning models.
- The Gluon training method makes it possible to train both the algorithm and neural network model simultaneously without compromising the overall performance.
- Developers can build models using various structure types and Python’s native control flow.
- Gluon allows programmers to define and manipulate deep learning models easily, making it an excellent tool for beginners in the deep learning framework space.
- With a highly flexible quotient, Gluon helps to streamline all prototype and experimental neural network models.
- Using dynamic graphs, Gluon empowers programmers to build models as they go, using any structure type and any of Python’s native control flow.
Sonnet is a deep learning framework built by a company named DeepMind. Sonnet, built on top of TensorFlow, is focused on constructing Python objects linked to a particular part of a neural network. This component collection uses a design for developing neural network structures within the TensorFlow framework.
- Sonnet is built around a single concept known as “snt.Module.” and provides developers with various self-sufficient models to use when building.
- Users have the option of using predefined modules such as snt.Linear, snt.Conv2D, or snt.BatchNorm, but they also have the freedom to build their modules from scratch.
- It comes with checkpointing, which can periodically save specific parameters during training and help provide backup data in case the program crashes or suddenly stops.
- Sonnet uses a design that works seamlessly within the TensorFlow structure, making it easier to access hidden details, including Tensors and variable_scopes.
- This framework creates models that can function with raw TensorFlow code and be input into various high-level libraries.
- With Sonnet, developers can declare other submodules internally or push them along to other modules during construction.
The MXNET deep learning framework uses a particular design to train and deploy deep neural networks. As one of the most scalable structures, MXNet provides some of the fastest model training compared to any other framework.
- MXNet can handle both imperative and symbolic programming; what that means is that developers have the freedom to pick which programming language when building their deep learning model.
- With MXNet, developers can work with state-of-the-art DL models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs).
- MXNet is highly portable and can quickly scale to a level that simultaneously uses several GPUs across various machines.
- As MXNet can support many different coding languages, it has earned a reputation for being one of the most flexible deep learning frameworks.
- Many developers favor MXNet over other frameworks primarily because of its superior natural language processing, forecasting, and speech/handwriting recognition.
The Caffe deep learning framework has a reputation for speed and application in modeling Convolution Neural Networks (CNN), making it one of the most popular options to date. For many developers, whether a project needs modeling CNNs or solving image processing problems, the Caffe library is known for having all the answers.
- Along with an extensive C++ library, Caffe uses a Python interface for data science.
- Caffe is typically much simpler to set up and train without the need to build onto the existing network.
- One of only a few deep learning frameworks, Caffe supports many programming languages such as C, C++, Python, MATLAB, and Command Line.
- Caffe offers programmers tremendous versatility in deep learning applications ranging from speech recognition and neural networks.
- Whether a project needs a framework to operate from different cloud-based platforms, Caffe provides smooth platform switching.
- The most significant of all these advantages is Caffe’s C++ library, which has access to the deep net repository, “Caffe Model Zoo,” which holds pre-trained networks for immediate use.
9. Deeplearning4j (DL4J)
The Deeplearning4J (DL4J) deep learning framework is a library designed for Java and JVM (Java Virtual Machine) programming languages. The unique part about DL4J is that it’s compatible with any JVM language, including Scala, Clojure, and Kotlin. Along with that, it also uses underlying computations written in C, C++, and Cuda.
Deeplearning4j (DL4J) Features:
- In The DL4J framework, all neural networks train in parallel using an iterative approach to reduce through clusters.
- The DL4J deep learning framework supports two primary types of neural network architectures: RNNs and CNNs.
- DL4J uses Java and Scala programming languages, making it possible to integrate alongside Hadoop and Apache Spark seamlessly.
Deeplearning4j (DL4J) Advantages:
- Deeplearning4j is easily one of the most scalable frameworks capable of processing large data sets.
- With lots of detailed framework documentation and a solid community to seek support, DL4J is an excellent option for all project types.
- As a top-tier commercial-grade, open-source framework, DL4J has one of the most extensive distributed deep learning libraries of any interface.
How to Choose a Deep Learning Framework?
There are several factors to consider when choosing the proper deep learning framework. The first aspect to think about is the main objective of this upcoming project, the programming language needed, and the skills needed to make it all possible. Along with that, it’s essential to examine the framework’s optimization for performance, ease of coding, and the ability for parallel processing.
Which Deep Learning Framework Is Easiest to Learn?
The deep learning framework that’s easiest to learn is Keras; this is because it’s straightforward but not unsophisticated. Compared to other frameworks, Keras gives a Python frontend with a significant degree of complexity with the capability to use multiple back-ends for computation. With over 350,000+ users and 700+ open-source contributors, it’s no wonder Keras is also one of the most popular.
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