As of today, both Machine Learning, as well as Predictive Analytics, are imbibed in the majority of business operations and have proved to be quite integral. However, it is Artificial Intelligence with the right deep learning frameworks, which amplifies the overall scale of what can be further achieved and obtained within those domains.
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Artificial intelligence and machine learning are no more mere buzzwords. In the last few years, the count of companies implementing machine learning algorithms to make sense of increasing amounts of data has grown exponentially.
The results and benefits of using machine learning models in business processes have brought a significant shift in the way companies are investing in these technologies.
Shallow architecture algorithms are being transformed into deep architecture models with multiple layers to create end-to-end learning and analyzing models. This has made applications smarter and more intelligent.
With unlimited application domains like value prediction, speech and image processing and recognition, natural language understanding, sentiment analysis, financial strategizing, gene mapping, fraud detection, translation, and more, deep learning is being extensively used by companies to train algorithms.
Given that deep learning is the key to executing tasks of a higher level of sophistication, building and deploying them successfully proves to be quite the herculean challenge for data scientists and data engineers across the globe. Today, we have a myriad of frameworks at our disposal that allows us to develop tools that can offer a better level of abstraction along with simplification of difficult programming challenges.
Each framework is built in a different manner for different purposes. Here, we look at some of the top 8 deep learning frameworks (in no particular order) for you to get a better idea on which framework will be the perfect fit or come handy in solving your business challenges.
TensorFlow is inarguably one of the most popular deep learning frameworks. Developed by the Google Brain team, TensorFlow supports languages such as Python, C++, and R to create deep learning models along with wrapper libraries. It is available on both desktop and mobile.
The most well-known use case of TensorFlow has got to be Google Translate coupled with capabilities such as natural language processing, text classification, summarization, speech/image/handwriting recognition, forecasting, and tagging.
TensorFlow’s visualization toolkit, TensorBoard, provides effective data visualization of network modeling and performance.
TensorFlow Serving, another tool of TensorFlow, is used for the rapid deployment of new algorithms/experiments while retaining the same server architecture and APIs. It also provides integration with other TensorFlow models, which is different from the conventional practices and can be extended to serve other models and data types.
TensorFlow is one of the most preferred deep learning frameworks as it is Python-based, supported by Google, and comes loaded with top-notch documentation and walkthroughs to guide you.
Highlights of TensorFlow
- Robust multiple GPU support
- Graph visualization and queues using TensorBoard
- Known to be complex and has a steep learning curve
- Excellent documentation and community support
Torch is a scientific computing framework that offers broad support for machine learning algorithms. It is a Lua based deep learning framework and is used widely amongst industry giants such as Facebook, Twitter, and Google.
It employs CUDA along with C/C++ libraries for the processing and was made to scale the production of building models and overall flexibility. As opposed to Torch, PyTorch runs on Python, which means that anyone with a basic understanding of Python can get started on building their deep learning models.
In recent years, PyTorch has seen a high level of adoption within the deep learning framework community and is considered to be quite the competitor to TensorFlow. PyTorch is basically a port to Torch deep learning framework used for constructing deep neural networks and executing tensor computations that are high in terms of complexity.
Given the PyTorch framework’s architectural style, the entire deep modeling process is far more straightforward as well as transparent in comparison to Torch.
Highlights of PyTorch
- Excellent at rapid prototyping
- Strong support for GPUs as parallel programs can be implemented on multiple GPUs
- Provides cleaner interface and is easier to use
- Facilitates the exchange of data with external libraries
The j in Deeplearning4j stands for Java. Needless to say, it is a deep learning library for the Java Virtual Machine (JVM). It is developed in Java and supports other JVM languages like Scala, Clojure, and Kotlin.
Parallel training through iterative reduces, micro-service architecture adaption coupled with distributed CPUs and GPUs are some of the salient features when it comes to Eclipse Deeplearning4j deep learning framework.
Widely adopted as a commercial, industry-focused, and distributed deep learning platform, Deeplearning4j comes with deep network support through RBM, DBN, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Recursive Neural Tensor Network (RNTN) and Long Short-Term Memory (LTSM).
Since this deep learning framework is implemented in Java, it is much more efficient in comparison to Python. When it comes to image recognition tasks using multiple GPUs, DL4J is as fast as Caffe. This framework shows matchless potential for image recognition, fraud detection, text-mining, parts of speech tagging, and natural language processing.
With Java as your core programming language, you should undoubtedly opt for this deep learning framework if you’re looking for a robust and effective method of deploying your deep learning models to production.
Highlights of DL4J
- Brings together the entire Java ecosystem to execute deep learning
- Can process massive amounts of data quickly
- Includes both multi-threaded and single-threaded deep learning frameworks
- Can be administered on top of Hadoop and Spark
4. THE MICROSOFT COGNITIVE TOOLKIT/CNTK
Popularly known for easy training and a combination of popular model types across servers, the Microsoft Cognitive Toolkit (earlier known as CNTK) is an open-source deep learning framework to train deep learning models. It performs efficient Convolution Neural Networks and training for image, speech, and text-based data.
Given its coherent use of resources, the implementation of Reinforcement Learning models or Generative Adversarial Networks (GANs) can be done quickly using the toolkit. The Microsoft Cognitive Toolkit is known to provide higher performance and scalability as compared to toolkits like Theano or TensorFlow while operating on multiple machines.
When it comes to inventing new complex layer types, the users don’t need to implement them in a low-level language due to the fine granularity of the building blocks. The Microsoft Cognitive Toolkit supports both RNN and CNN type of neural models and is thus capable of handling image, handwriting, and speech recognition problems. Currently, due to the lack of support on ARM architecture, the capability on mobile is relatively limited.
Highlights of The Microsoft Cognitive Toolkit
- Highly efficient and scalable for multiple machines
- Supported by interfaces such as Python, C++, and Command Line
- Fit for image, handwriting and speech recognition use cases
- Supports both RNN and CNN type of neural networks
Keras library was developed, keeping quick experimentation as its USP. Written in Python, the Keras neural networks library supports both convolutional and recurrent networks that are capable of running on either TensorFlow or Theano.
As the TensorFlow interface is tad challenging and can be intricate for new users, Keras deep learning framework was built to provide a simplistic interface for quick prototyping by constructing active neural networks that can work with TensorFlow.
In a nutshell, Keras is lightweight, easy-to-use, and has a minimalist approach. These are the very reasons as to why Keras is a part of TensorFlow’s core API.
The primary usage of Keras is in classification, text generation, and summarization, tagging, translation along with speech recognition, and others. If you happen to be a developer with some experience in Python and wish to delve into deep learning, Keras is something you should definitely check out.
Highlights of Keras
- Easy-to-understand and consistent APIs
- Seamlessly integrates with TensorFlow workflow.
- Supports multiple deep learning backends
- Built-in support for distributed training and multi-GPU parallelism
ONNX or the Open Neural Network Exchange was developed as an open-source deep learning ecosystem. Developed by Microsoft and Facebook, ONNX proves to be a deep learning framework that enables developers to switch easily between platforms.
This deep learning framework comes with definitions on in-built operators, standard data types as well as definitions of an expandable computation graph model. ONNX models are natively supported in The Microsoft Cognitive Toolkit, Caffe2, MXNet, and PyTorch. It also provides converters for different machine learning frameworks like TensorFlow, CoreML, Keras, and Sci-kit Learn.
ONNX has gained popularity owing to its flexibility and interoperability. Using ONNX, one can easily convert their pre-trained model into a file, which can then be merged with their app. ONNX is a powerful tool that prevents framework lock-in by providing easier access to hardware optimization and enabling model sharing.
Highlights of ONNX
- Provides interoperability and flexibility
- Provides compatible runtimes and libraries
- Liberty of using the preferred framework with a selected inference engine
- Maximizes performance across hardware
Designed specifically for high efficiency, productivity, and flexibility, MXNet (pronounced as mix-net) is a deep learning framework that is supported by Python, R, C++, and Julia.
What makes MXNet one of the most preferred deep learning frameworks is its functionality of distributed training. It provides near-linear scaling efficiency, which utilizes the hardware to its greatest extent.
It also enables the user to code in a variety of programming languages (Python, C++, R, Julia, and Scala, to name a few). This means that you can train your deep learning models with whichever language you are comfortable in without having to learn something new from scratch.
With the backend written in C++ and CUDA, MXNet is able to scale and work with a myriad of GPUs, which makes it indispensable to enterprises. Case in point – Amazon employed MXNet as its reference library for deep learning.
MXNet supports Long Short-Term Memory (LTSM) networks, along with both RNN and CNN. This deep learning framework is known for its capabilities in imaging, handwriting/speech recognition, forecasting as well as NLP.
Highlights of MXNet
- Hybrid programming which provides the best of both imperative and symbolic programming
- Provides distributed training
- Supports deployment in different languages such as Java, Scala, R, Julia, C++, Perl, and Clojure
- Nearly linear on GPU clusters which provides excellent scalability
Well known for its laser-like speed, Caffe is a deep learning framework that is supported with interfaces like C, C++, Python, MATLAB, and Command Line. Its applicability in modeling Convolution Neural Networks (CNN) and its speed has made it popular in recent years.
The most significant benefit of using Caffe’s C++ library is accessing the deep net repository ‘Caffe Model Zoo.’ Caffe Model Zoo contains networks that are pre-trained and can be used immediately. Whether it is modeling CNNs or solving image processing issues, this has got to be the go-to library.
Caffe’s biggest USP is speed. It can process over sixty million images on a daily basis with a single Nvidia K40 GPU. That’s 1 ms/image for inference, and 4 ms/image for learning and more recent library versions are even faster.
Caffe is a popular deep learning network for vision recognition. However, Caffe does not support fine granularity network layers like those found in TensorFlow or CNTK. Given the architecture, the overall support for recurrent networks and language modeling is quite poor, and establishing complex layer types has to be done in a low-level language.
Highlights of Caffe
- C++ library comes with a Python interface
- The configuration defines models without hard-coding.
- Easier to set up and train, without having to build onto the network
- Support for recurrent neural networks is quite poor
To Sum It Up
It is reasonably evident that the advent of Deep Learning has initiated many practical use cases of Machine Learning and Artificial Intelligence in general. Breaking down tasks in the simplest ways in order to assist machines in the most efficient manner has been made likely by Deep Learning.
That being said, which deep learning framework from the above list would best suit your requirements? The answer to that lies on a number of factors, however, if you are looking to just get started, then a Python based deep learning framework like TensorFlow or Chainer should be your choice. If you happen to be seasoned, you need to consider speed, resource requirement, and usage along with the coherence of the trained model before picking out the best deep learning framework.