Best Deep Learning Frameworks in October, 2019

Best Deep Learning Framework

With an ever increasing number of organizations hoping to scale up their activities, it has turned out to be basic for them to soak up both machine learning just as predictive analytics. Artificial intelligence combined with the correct deep learning framework has genuinely intensified the general size of what organizations can accomplish and get inside their domains.

The AI worldview is consistently advancing. The key is to move towards creating AI models that keep running on versatile so as to make applications more brilliant and unquestionably increasingly shrewd. Deep learning is the thing that makes taking care of complex issues conceivable.

Deep Learning is essentially Machine Learning on steroids.

Each framework is built in a different manner for different purposes. Here are the Top and Best deep learning frameworks listed which will be the perfect fit in solving your business challenges.

 

Best Deep Learning Frameworks in 2019

 

TensorFlow

Complete Guide to TensorFlow for DeepLearning with Python

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. Wikipedia

TensorFlow is arguably one of the best deep learning frameworks and has been adopted by several giants 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.

 

Caffe

10-top-open-source-artificial-intelligence-tools

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.

Check out our web image classification demo!

Caffe is a deep learning framework that is supported with interfaces like C, C++, Python, and MATLAB as well as the command line interface. It is well known for its speed and transposability and its applicability in modeling convolution neural networks (CNN).

The biggest benefit of using Caffe’s C++ library is the ability to access available networks from the deep net repository Caffe Model Zoo that are well trained and can be utilized immediately. When it comes to modeling CNNs or solving image processing issues, this should be your always first go-to library.

Caffe’s biggest USP is speed. It can process over 50 million images on a daily basis with a single Nvidia K40 GPU  and few more recent library versions are doing faster still.

Caffe is a famous deep learning network for visual recognition.

 

 

Torch/PyTorch

pytorch

Torch  is an open-source machine learning library, a scientific computing framework, and a script language based on the Lua programming language. It provides a wide range of algorithms for deep learning, and uses the scripting language LuaJIT, and an underlying C implementation.Wikipedia

As of late, PyTorch  is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. PyTorch has two main features: Tensor computation (like NumPy) with strong GPU acceleration. Automatic differentiation for building and training neural networks.

 

 

Microsoft Cognitive Toolkit/CNTK

CNTK

Microsoft Cognitive Toolkit, previously known as CNTK and sometimes styled as The Microsoft Cognitive Toolkit, is a deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.Wikipedia

The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs).

CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

This video provides a high-level overview of the toolkit. In addition, Microsoft offers an introductory course to deep learning with CNTK, Deep Learning Explained.

 

 

 

 

MXNet

mxnet

Apache MXNet is an open-source deep learningsoftware framework, used to train, and deploy deep neural networks. … The MXNet library is portable and can scale to multiple GPUs and multiple machines.

Designed specifically for the purpose of high efficiency, productivity, and flexibility, MXNet(pronounced as mix-net) is a deep learning framework supported by Python, R, C++, and Julia.

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 RNNs and CNNs.

This deep learning framework is best in imaging, handwriting/speech recognition, forecasting, and NLP.

 

 

Chainer

chainer

Chainer  is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries. … Chainer is notable for its early adoption of “define-by-run” scheme, as well as its performance on large scale systems.

 

BRIDGE THE GAP BETWEEN ALGORITHMS AND IMPLEMENTATIONS OF DEEP LEARNING

Chainer is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBMIntelMicrosoft, and Nvidia.

 

 

 

 Keras

Advanced Deep Learning with Keras

Keras  is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. … Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlowCNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

  • Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
  • Supports both convolutional networks and recurrent networks, as well as combinations of the two.
  • Runs seamlessly on CPU and GPU.

 

Deeplearning4j

Deeplearning4J

Eclipse Deeplearning4j is a deep learning programming library written for Java and the Java virtual machine (JVM) and a computing framework with wide support for deep learning algorithms. … These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

Deeplearning4j is open-source software released undeApache License 2.0,developed mainly by a machine learning group headquartered in San Francisco and Tokyo and led by Adam Gibson. It is supported commercially by the startup Skymind, which bundles DL4J, TensorflowKeras and other deep learning libraries in an enterprise distribution called the Skymind Intelligence Layer. Deeplearning4j was contributed to the Eclipse Foundation in October 2017. Source Wiki

 

FINAL WORDS

Are there any other deep learning frameworks you’ve worked on? I would love to hear your thoughts and feedback on that plus the ones we covered in this article. Connect with me in the comments section below.

And remember, these frameworks are essentially just tools that help us get to the end goal. Choosing them wisely can reduce a lot of effort and time.

 

 

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