AI News, Some Lesser-Known Deep Learning Libraries

Some Lesser-Known Deep Learning Libraries

This is a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph.

Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.

nmtpy is a suite of Python tools, primarily based on the starter code provided in dl4mt-tutorial for training neural machine translation networks using Theano.

This repository aims at providing a high performing and flexible deep learning platform, by prototyping a pure NumPy interface above MXNet backend.

keras-rl implements state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras.

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Skymind bundles Deeplearning4j and Python deep learning libraries such as Tensorflow and Keras (using a managed Conda environment) in the Skymind Intelligence Layer (SKIL), which offers ETL, training and one-click deployment on a managed GPU cluster.

DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework that solves problems involving massive amounts of data in a reasonable amount of time.

For more information on benchmarking Deeplearning4j, please see this benchmarks page to optimize its performance by adjusting the JVM’s heap space, garbage collection algorithm, memory management and DL4J’s ETL pipeline.

PyTorch offers dynamic computation graphs, which let you process variable-length inputs and outputs, which is useful when working with RNNs, for example.

Since it’s introduction, PyTorch has quickly become the favorite among machine-learning researchers, because it allows certain complex architectures to be built easily.

Some version of it is used by large tech companies such as Facebook and Twitter, which devote in-house teams to customizing their deep learning platforms.

Torch, while powerful, was not designed to be widely accessible to the Python-based academic community, nor to corporate software engineers, whose lingua franca is Java.

And we believe that a commercially supported open-source framework is the appropriate solution to ensure working tools and building a community.

Pros and Cons: Pros and Cons Caffe is a well-known and widely used machine-vision library that ported Matlab’s implementation of fast convolutional nets to C and C++ (see Steve Yegge’s rant about porting C++ from chip to chip if you want to consider the tradeoffs between speed and this particular form of technical debt).

In contrast to Caffe, Deeplearning4j offers parallel GPU support for an arbitrary number of chips, as well as many, seemingly trivial, features that make deep learning run more smoothly on multiple GPU clusters in parallel.

(As of March 2016, another Theano-related library, Pylearn2, appears to be dead.) In contrast, Deeplearning4j brings deep learning to production environment to create solutions in JVM languages like Java and Scala.

This license does not apply to the method by which CNTK makes distributed training easy – one-bit SGD – which is not licensed for commercial use.

Chainer is an open-source neural network framework with a Python API, whose core team of developers work at Preferred Networks, a machine-learning startup based in Tokyo drawing its engineers largely from the University of Tokyo.

Until the advent of DyNet at CMU, and PyTorch at Facebook, Chainer was the leading neural network framework for dynamic computation graphs, or nets that allowed for input of varying length, a popular feature for NLP tasks.

On a business level, Gluon is an attempt by Amazon and Microsoft to carve out a user base separate from TensorFlow and Keras, as both camps seek to control the API that mediates UX and neural net training.

That is, anyone is free to make and patent derivative works based on Apache 2.0-licensed code, but if they sue someone else over patent claims regarding the original code (DL4J in this case), they immediately lose all patent claim to it.

(In other words, you are given resources to defend yourself in litigation, and discouraged from attacking others.) BSD doesn’t typically address this issue.

That is, we automate the setting up of worker nodes and connections, allowing users to bypass libs while creating a massively parallel network on Spark, Hadoop, or with Akka and AWS.

(When we talk about operations, we also consider things like strings and other tasks involved with higher-level machine learning processes.) Most deep-learning projects that are initially written in Python will have to be rewritten if they are to be put in production.

Many Python programmers opt to do deep learning in Scala because they prefer static typing and functional programming when working with others on a shared code base.

Finally, Java is a secure, network language that inherently works cross-platform on Linux servers, Windows and OSX desktops, Android phones and in the low-memory sensors of the Internet of Things via embedded Java.

While Torch and Pylearn2 optimize via C++, which presents difficulties for those who try to optimize and maintain it, Java is a “write once, run anywhere” language suitable for companies who need to use deep learning on many platforms.

In sum, Java boasts a highly tested infrastructure for pretty much any application, and deep-learning nets written in Java can live close to the data, which makes programmers’ lives easier.


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It

between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We

Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join

Please cite Caffe in your publications if it helps your research: If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by Google Scholar.

The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI Trevor Darrell for guidance.

8 Best Deep Learning Frameworks for Data Science enthusiasts

With more and more businesses looking to scale up their operations, it has become integral for them to imbibe both machine learning as well as predictive analytics.

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.

Here, we look at the 8 deep learning frameworks to give you a better idea of which framework will be the perfect fit or come handy in solving your business challenges.

TensorFlow comes with two tools that are widely used: If you’re taking your first steps toward deep learning, it is a no-brainer to opt for TensorFlow given that is Python-based, is supported by Google, and comes loaded with documentation and walkthroughs to guide you.

The biggest benefit of using Caffe’s C++ library (comes with a Python interface) is the ability to access available networks from the deep net repository Caffe Model Zoo that are pre-trained and can be used immediately.

Given the architecture, the overall support for recurrent networks, and language modeling it’s quite poor, and establishing complex layer types has to be done in a low-level language.

Popularly known for easy training and the combination of popular model types across servers, the Microsoft Cognitive Toolkit (previously known as CNTK) is an open-source deep learning framework to train deep learning models.

Compared to Caffe, when it comes to inventing new complex layer types, users don’t need to implement them in a low-level language due to the fine granularity of the building blocks.

PyTorch is basically a port to the Torch deep learning framework used for constructing deep neural networks and executing tensor computations that are high in terms of complexity.

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.

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.

Deeplearning4j comes with a deep network support through RBM, DBN, convolution neural networks (CNNs), recurrent neural networks (RNNs), recursive neural tensor networks (RNTNs), and long short-term memory (LTSM).

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.

If you’re looking for something more, in that case - speed, resource requirement, and usage along with the coherence of the trained model should always be considered prior to picking out the best deep learning framework for your business needs.

The algorithm was put in place mainly to assess and flag images that were not associated with cars and thus, maintaining the quality and precision of image related data was taken care of.

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