As of today, both Machine Learning as well as Predictive Analytics are imbibed in majority of business operations and have proved to be quite integral. However, it is Artificial Intelligence with the right deep learning framework which amplifies the overall scale of what can be further achieved and obtained within those domains. The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile so as to make applications smarter and far more intelligent.
Delving even further, deep learning is what makes solving complex problems possible.
Given that deep learning is the key to executing tasks of 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 order for you to get a better idea on which framework will be the perfect fit or come handy in solving your business challenges.
Arguably one of the best deep learning frameworks that now been adopted by several giants at scale such as Airbus, Twitter, IBM and others mainly due to its highly flexible system architecture.
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 is available on both desktop and mobile and also supports languages such as Python, C++ and R to create deep learning models along with wrapper libraries.
TensorFlow comes with 2 tools which are widely used –
- TensorBoard for effective data visualization of network modeling and performance
- TensorFlow Serving for 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 model and data types.
If you happen to be taking your first steps when it comes to deep learning, it is a no brainer that you should opt for TensorFlow given that is Python based, supported by Google and comes loaded with documentation and walkthroughs to guide you.
Caffe is a deep learning framework that is supported with interfaces like C, C++, Python, MATLAB as well as the Command Line Interface. It is well known for its speed and transposability and its applicability in modelling Convolution Neural Networks (CNN). The biggest benefit of using Caffe’s C++ library (comes with a Python interface) is accessing available networks from the deep net repository ‘Caffe Model Zoo’ which are pre-trained and can be used immediately. Whether it is modelling 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 faster still.
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 low-level language.
The Microsoft Cognitive Toolkit/CNTK
Popularly known for easy training and 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. Similar to Caffe, it is supported by interfaces such as Python, C++ and the Command Line Interface.
Given its coherent use of resources, the implementation of Reinforcement Learning models or Generative Adversarial Networks (GANs) can be done easily using the toolkit. It is known to provide higher performance and scalability as compared to toolkits like Theano or TensorFlow while operating on multiple machines.
Compared to Caffe, 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 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 fairly limited.
Torch is a scientific computing framework that offers wide 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 basically made to scale production of building models and overall flexibility.
Offlate, 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.
As opposed to Torch, PyTorch runs on Python which means that anyone with basic understanding of Python can get started on building their own deep learning models.
Given PyTorch framework’s architectural style, the entire deep modeling process is far more simpler as well as transparent in comparison to Torch.
Designed specifically for the purpose of high efficiency, productivity and flexibility, MXNet (pronounced as mix-net) is a deep learning framework which is supported by Python, R, C++ and Julia.
The beauty of MXNet is that it gives the user the ability 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.
Highly powerful, dynamic and intuitive – Chainer is a Python based deep learning framework for neural networks that is designed on the define by run strategy. Compared to other frameworks that use the same strategy, you can modify the networks during runtime, thus allowing you to execute arbitrary control flow statements.
Chainer supports both CUDA computation along with multi-GPU. This deep learning framework is utilized mainly for sentiment analysis, machine translation, speech recognition etc.using RNN and CNN.
Well known for being minimalist, the Keras neural networks library (supporting interface – Python) supports both convolutional and recurrent networks that are capable of running on either TensorFlow or Theano. The library is written in Python and was developed keeping quick experimentation as its USP.
Due to the fact that the TensorFlow interface is a tad bit challenging coupled with the fact that it is a low-level library that can be intricate for new users, Keras was built to provide a simplistic interface for the purpose of quick prototyping by constructing effective neural networks that can work with TensorFlow.
Lightweight, easy to use and really straightforward when it comes to building a deep learning model by stacking multiple layers – that is Keras in a nutshell. These are the very reasons as to why Keras is a part of TensorFlow’s core API.
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 on Python and wish to delve into deep learning, Keras is something you should definitely check out.
Parallel training through iterative reduce, micro-service architecture adaption coupled with distributed CPUs and GPUs are some of the salient features when it comes to Deeplearning4j deep learning framework. It is developed in Java as well as Scala and supports other JVM languages too.
Widely adopted as a commercial, industry-focused distributed deep learning platform, the biggest advantage of this deep learning framework is that you can bring together the entire Java ecosystem to execute deep learning and that it can be administered on top of Hadoop and Spark to orchestrate multiple host threads. DL4J uses map reduce so as to train the network while depending on other libraries to execute large matrix operations.
Deeplearning4j comes with a 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, it 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 certainly opt for this deep learning framework if you’re looking for a robust and effective method of deploying your deep learning models to production.
It is fairly evident that the advent of Deep Learning has initiated many practical use cases of Machine Learning and Artificial Intelligence in general. Breaking down of 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.