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Top 5 Machine Learning Frameworks for 2019

Choosing one right Machine Learning framework can be an intimidating task as different companies have different priorities when it comes to machine learning. Some may prefer to use a framework that is easy to learn and utilize, whereas others might give priority to parameter optimization or production deployment. In concise, Machine learning is branch of computer science whereby a machine is given the capability to the ability to self-improve without direct human supervision.

When delving into the world of machine learning (ML), each of the Frameworks is different from each other. Especially if you’re a beginner, there are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing for your web development company needs is not an easy task and hence can create a lot of perplexity in the mind of the user.

Many renowned Companies such as Amazon, Netflix, YouTube benefits from machine learning to anticipate what sort of content elevates the interest of the audience the most or to determine the probability that you will click on a specific digital commercial. Machine learning algorithm processes large volumes of unstructured information, and turn them into actionable insights.

Presently, the hottest trends in software Development is machine learning. It’s a buzzword that is popping up more and more all the time due to popular recent innovations, like self-driving cars, yet so many people don’t know what it really is. In this blog, we’ll discuss some major machine learning frameworks and if you are unassertive about which ML frameworks to use, let’s parse through the top 5 machine learning frameworks list based on a variety of parameters.

  • Microsoft Cognitive Toolkit.
  • TensorFlow.
  • Apache Mahout.
  • Caffe.
  • Apache Singa.

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit (formerly known as CNTK) supports coding in Python and C++ used for training algorithms to learn like a human brain. This tool is design to utilize various use of this framework models like convolutional neural networks, feed-forwards DNNs, and recurrent neural networks.

Benefits of Microsoft Cognitive Toolkit:

  • CNTK can be used for text and image training
  • Machine Learning is one of the most dynamic frameworks as it supports multiple hardware types, including various CPUs and GPUs.
  • One of the most important highlights of CNTK is Compatibility and Customizable as it is the most expressive and allow you to choose your own parameters to use machine learning architecture.

TensorFlow

TensorFlow is an open-source software and one of the most popular machine learning frameworks for Java development. TensorFlow is a Google’s flagship framework for building machine learning applications. Python is the primary language with secondary support for C++, Java, and Go. Though, it offers an environment for creating algorithms for solving a variety of tasks, including natural language processing, speech/ image/ handwriting recognition, text classification, forecasting, and tagging but to understand it’s complexity functions at an early stages is a bit hard.

Benefits of TensorFlow

  • It is available and run on both desktop and mobile platforms.
  • It is highly flexible for developers to implement computations on one or more GPUs or CPUs by using a single API and makes the tasks much more easier.
  • It uses a static graph with plenty of higher levels of abstraction available in the form of libraries such as the popular high-level neural network API Keras, Sonnet, and TFLearn.
  • TensorFlow provides Research and development, Auto differentiation and Performance at its best.

Apache Singa

Apache Singa is creating for open-source machine learning libraries and focuses on distributed deep learning using model partitioning and parallelizing the training process. C++ is used for SINGA with Python and Java as well.

Benefits of Apache Singa:

  • Due to its flexible architecture, it can run both synchronous and asynchronous and even hybrid training methods.
  • Three important components of tech stack of are: IO, Model and Core. Io model contains classes which are used for reading and writing data to disk and network. The Core component in IO model handles the memory management operations and Tensor functions. The model also includes the algorithm and the data structures used for machine learning
  • Image recognition and natural language processing (NLP), are the main applications of SINGA.

CAFFE2

CAFFE (Convolutional Architecture for Fast Feature Embedding) is an open source framework and this framework is principally written in CPP. It is primary use is to design start-ups Prototypes. It supports many different types of architectures mainly on image classification and segmentation and is fully connected neural network designs, it offers GPU as well as CPU based acceleration as well like TensorFlow. Hence is one of the fastest ways to apply neural networks to the problem and fastest convent implementations available.

Benefits of Caffe Framework:

  • Caffe framework provides well organized Mat lab and python interface
  • Caffe is very lightweight and efficient for deployment, but rather limited in flexibility.
  • It provides an easy and straightforward way to experiment with deep learning and leverage community contributions of new models and algorithms.

PyTorch

PyTorch is considered to be the main rival to TensorFlow. As provides two high-level features: tensor computation with strong GPU acceleration, and deep neural networks built on a tape-based auto diff system. Simplicity machine learning framework is the important attribute as it helps to easily set up and get going fast.

Advantages of torch framework:

  • Torch is very flexible to use and making it easy to learn for Python developers.
  • PyTorch main advantages includes high level of speed and GPU utilization efficiency, and premade models for data training.
  • PyTorch uses dynamic graphs, and are built with the normal Python programming control flow.
  • Torch has a great community support as well as ample sample code repository. torch is among the friendliest languages as the error message contains detail information of the same.

Wrap up…

In a nutshell, the purpose of this article is to provide an overview of the frameworks that will remain trending in 2019, of course as web development with machine learning is going to revolutionize the IT world. Choosing the right machine learning framework for your development needs careful consideration and that best suits your workflow.

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