Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based onl earning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
why deep learning ?
The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don’t perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly.
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.
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convnet implementations available.
Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook’s artificial-intelligence research group, and Uber’s “Pyro” software for probabilistic programming is built on it.
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.