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Top 10 Most Used Machine Learning Frameworks you need to learn

Last updated on March 1, 2021, 12:15 p.m. 2456 Views

Kirandeep Kaur

Kirandeep Kaur |

3+ Technical Content Writer. and who is passionate about his career

Top 10 Most Used Machine Learning Frameworks you need to learn

Last updated on March 1, 2021, 12:15 p.m. 2456 Views

Kirandeep Kaur


Top 10 Most Used Machine Learning Frameworks you need to learn

Machine Learning Definition :

Everyone is making predictions for the upcoming years but these people are shaping the future today with the AI treasure and some of the predictions are advancing in the subfields like supervised learning, semi-supervised learning, neural symbolic, virtual machine learning frameworks are expected to increase more. There are several machine learning frameworks that are developed for the deployment of AI applications. The purpose of using machine learning frameworks is to grunt the work of testing and configuring AI workloads of optimization, production, and experimentation.

Several popular machine learning frameworks include TensorFlow, MXNet, Scikit-learn, Keras, PyTorch, and much more. The machine learning frameworks are commonly used by data scientists to coach algorithms to perform use cases including prediction, image recognition, and recommendation. The following diagram explains the classification of machine learning models.

diagram explains the classification of machine learning modules

Key features of machine learning frameworks

  • Optimal performance
  • Developer friendly
  • Easy to understand and code
  • Parallelization to distribute the computation process
  • Steady user-base and support community

Machine Learning Frameworks in Real Life

If you have utilized Netflix, at that point you should realize that it suggests you a few pictures or shows for viewing dependent on what you have observed before. Machine learning is utilized for this proposal and to choose the information which coordinates your decision. It utilizes the previous information. 

Facebook is another example. At the point when you upload a picture on Facebook, it can perceive an individual in that picture and suggest mutual friends. Machine learning, in that case, is utilized for these forecasts. It utilizes information like your friend list, available pictures, and so on and it makes predictions dependent on that.

Machine Learning Frameworks

Are you planning to venture into machine learning? The following curated list of machine learning frameworks will help you know and choose the best framework you are required in your project. Here are they:

  1. Google TensorFlow
  2. PyTorch
  3. Caffe
  4. Apache Spark MLlib
  5. Amazon Machine Learning
  6. Microsoft Distributed Machine Learning Toolkit
  7. Microsoft Computational Network Toolkit
  8. Microsoft Cognitive Toolkit
  9. Colab
  10. Weka

Let’s get into the details now.

  1. Google TensorFlow

Google’s TensorFlow is an open-source machine learning framework that supports platforms like Linux, Mac OS, and Windows. It is written with the usage of programming languages like Python, C++, and CUDA. The primary feature of TensorFlow is that it provides a library for dataflow programming. TensorFlow offers multiple models and versions that are used simultaneously for research and development purposes. TensorFlow is known to be a very flexible and portable framework that can be run on GPU, CPU, servers, desktops, and mobile devices. It has complicated functions and paradigms and supports regressions, classifications, and neural networks. The TensorFlow framework is primarily used for numerical computations where batches of data or sensors are processed by a series of algorithms described by a graph.

  1. PyTorch

The torch is an open-source machine learning library that is utilized for scientific computing and a scripting language based on Lua programming language. PyTorch supports the platforms like Linux, Mac OS, and Windows. It is written with the usage of programming languages like Python, C++, and CUDA. The primary feature of PyTorch is that it provides an auto grad module, optim module, and a module for the dataflow programming.

  1. Caffe

Caffe is a most popular deep learning framework that is made with a combination of 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 Ph.D. at UC Berkeley. Caffe is released under the BSD 2-Clause license. Caffe supports interfaces like C, C++, Python, MATLAB, and command-line interface as well. Caffe is well-known for its speed and transposability in modeling convolution neural networks (CNN). The major benefit of using Caffe’s C++ library is the capability to access available networks. The ultimate speed of Caffe is its USP asit can process over 50 million images on a daily basis with a single Nvidia K40 GPU  and a few more recent library versions are doing faster still. Caffe is used for the implementation of visual recognition in deep learning.

  1. Apache Spark MLlib

Apache Spark is best known for being a part of the Hadoop ecosystem, but this in-memory data processing framework was born outside of the Hadoop family. Spark has become a go-to machine learning framework because of its growing library of algorithms that can be applied to in-memory data at high speed. Spark is written using programming languages like Java, Scala, R, and Python. MLlib contains learning algorithms and utilities, which include clustering, regression, collaborative filtering, classification, dimensionality reduction, low-level optimization primitives, and high-level pipeline APIs.

  1. Amazon Machine Learning

Amazon Machine Learning is a visualization tool that is used to create machine learning models without the foundation level knowledge of complex machine learning algorithms. Developers with any skill set can use this framework to learn and develop machine learning models. It is used to build binary classification, multi-class categorization as well as regression on data to create a model. The resulting imported or exported datasets for training models should not be larger than 100GB.

  1. Microsoft Distributed Machine Learning Toolkit

The more computers you need to toss at any machine learning issue, the better—creating ML applications that run well across huge quantities of machines can be tricky. Microsoft's DMTK (Distributed Machine Learning Toolkit) framework handles the issue of dispersing different sorts of machine learning employments over a group of frameworks. DMTK is charged as a structure as opposed to an out and out-of-the-container arrangement, so the quantity of calculations included with it is little. Notwithstanding, you will discover key machine learning libraries, for example, gradient boosting framework (LightGBM) and backing for a couple of deep learning frameworks like Torch and Theano. The structure of DMTK allows users to take advantage of groups with restricted assets. For example, each node in the cluster has a local cache, reducing the amount of traffic with the central server node that provides parameters for the job in question.

  1. Microsoft Computational Network Toolkit

Hot closely following the release of DMTK, Microsoft divulged one more machine learning framework, the Computational Network Toolkit. The computational network toolkit is like Google TensorFlow in that it lets clients make neural systems by method of a coordinated diagram. Microsoft likewise considers it to be tantamount to ventures like Caffe, Theano, and Torch – aside from the capacity of Computational network toolkit to accomplish more prominent speed by accelerating both various CPUs and numerous GPUs in equal. Microsoft claims that running CNTK on GPU groups on Azure permitted it to quicken discourse acknowledgment preparing for Cortana by a significant degree. The most recent release of the structure, CNTK 2.0, increases pressure on TensorFlow by improving exactness, including a Java API for Spark similarity, and supporting code from the Keras system (generally utilized with TensorFlow).

  1. Microsoft Cognitive Toolkit (CNTK)

MS CNTK or Cognitive Toolkit is another machine learning framework that is developed by Microsoft for deep learning and training algorithms. It is used to construct machine learning models that can be learned like a human brain.

  1. Colab

Colab is introduced by Google and supports the cloud services platform to execute machine learning tasks. The Colab supports libraries like PyTorch, Keras, TensorFlow, and OpenCV. It has features as it helps in educating machine learning and it assists in machine learning research.

  1. Weka

Weka supports the platforms like Linux, Mac OS, and Windows. It is written with the usage of Java programming language. Weka is a machine learning software in Java that has a wide range of machine learning algorithms for data mining tasks. You can use this for your research, education, and applications. This software is platform-independent and easy to use. Also, it is flexible for scripting experiments. The primary features of Weka are-

  • Data preparation
  • Data classification
  • Regression
  • Clustering
  • Data Visualization
  • Association rules mining
  • Graphical user interfaces including data visualization
  • Environment for comparing learning algorithms

What future beholds machine learning frameworks?

Currently, TensorFlow and PyTorch are the super exciting frameworks that can be proficiently used in the future. The win of the frameworks is in the virtue of its design. Each of the machine learning frameworks has its respective territories marked out – one side owns research, the other side owns the industry. Machine learning is still in the research-driven field. The industry cannot afford to ignore the research output of the machine learning frameworks. Machine learning research itself is in a massive state of flux. With time, not only the changes in frameworks are made but the models/paradigms, the hardware used in upcoming years may look drastically different than the ones we have currently.

Tips to choose the best machine learning framework

  • The framework should serve as the best available replacement that exists for conventional data mining methods.
  • It should provide extensive security and protection from different types of security threats
  • It should provide extensive security and protection from different types of security threats
  • It should be able to produce customized information and content
  • It should provide ample amounts of ML API
  • It should be useful and efficient for understanding customer behavior
  • It should be useful when the need is for quicker product discovery

Ask yourself the following questions before choosing a machine learning framework:

  • Will the framework be used for classical machine learning algorithms or deep learning?
  • Which will be the preferred programming language I will be using to develop machine learning models?
  • What type of hardware, software, or cloud services will I be using for scaling?

As a machine learning engineer, asking yourself these questions will help you choose an ideal framework based on your needs. The programming languages like Python and R are the most used software programming languages in machine learning, but other machine learning engineers also prefer to use C language, Java, or Scala-like programming languages as well. Currently, most of the applications are written in Python programming language rather than R because of its simplicity and elegance.

If you are among those machine learning engineers who prefer to use Python programming language to build their ML model, then Caffe and Google TensorFlow are the popular choices among Python coders or Pythonists creating ML models.

Tell us in the comment section which one is your favorite machine learning framework. Happy reading!

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