Top 15 Best Machine Learning Frameworks In 2020

“Machine intelligence is that last invention that humanity will ever need to make.”
– Nick Bostrom

Machine Learning (ML) is on a roll right now. Every nook and corner of global business segments showcase Machine Learning, Deep Learning, Artificial Intelligence Services and more, in their regular functioning. The world is now understanding the significance of ML and how best suited it is for a technically driven society.

Machine Learning that is prevalent today is totally focussed on innovative computing advancements based on pattern recognition. It has been driving technology stalwarts like Google, Facebook, YouTube, Uber Eats, Apple’s Siri, Amazon’s Alexa and many more.

What Is Machine Learning?

“Machine Learning will automate jobs that most people thought could only be done by people.” – Dave Waters

Machine Learning is a straight AI-based application that gives the capabilities of understanding and enhancing from experience, excluding programming-based tasks. It includes detailed investigation, makes the most of data and extracts rules and logic behind problems. There is a huge leverage of statistics that help in culling out patterns from the big deluge of data.

Types Of Machine Learning:

  • Supervised:

Mainly based on labelled data. Here the dataset acts as a teacher or an instructor.

  • Unsupervised:

A type of machine learning which learns from unlabeled data, decoding hidden patterns and data structures.

  • Reinforcement:

It is based on learning from mistakes. Here the algorithm mainly learns through trails and errors.

Wonder Why Machine Learning Is So Popular Today? Here Is Why:

  • Assists multiple industries in their major functioning – enhancing precision of financial models, detailed medical analytics, creation of effective predictive and preventive maintenance planning and many more
  • It modernizes data entry processes and thereby prevents replication
  • Easily finds out trash emails and spams
  • Continuous improvement of procedures and data outputs
  • Helps in replacing human involvement for major functionalities

Top_15_Machine_Learning_Frameworks

Top Machine Learning Frameworks To Look For:

  • TensorFlow
  • Sonnet
  • PyTorch
  • Keras
  • Scikit-learn
  • Caffe
  • Apache Mahout
  • Apache Spark
  • Accord .NET
  • Microsoft Cognitive Toolkit
  • MXNet
  • Gluon
  • Firebase ML Kit
  • Shogun
  • Apple’s Core ML
TensorFlow:

TensorFlow is a popular, open source, free framework that offers many tools and libraries for Machine Learning. Based on JavaScript, it helps in building models with high quality APIs and offers many generalized options to choose from.

It facilitates deployment of ML models across your devices, cloud, or the browser directly. It is suggested to use TensorFlow Extended (TFX) for a comprehensive feel and TensorFlow Lite for utilization on mobile devices. Learners may need knowledge of Julia, Go and Python, to get expertise in TensorFlow. It is useful in SQL tables, data integration functions and images, all together.

Sonnet:

Considered as a simple, powerful, high-end framework for ML, Sonnet is best utilized for creating complicated neural network structures in TensorFlow. It is easy to use, integrate and possesses efficient libraries to support.

Built by DeepMind, it is possible to create modules that can search for relevant sub modules within and can pass onto different modules while creation. Since it is built on top of TensorFlow, there is development of primary Python objects based on neural networks.

It leverages a simple concept of ‘snt.Module’ that is self-sufficient and decoupled from each other. Since it is means to execute seamlessly with TensorFlow, there is easy access to Tensors and other variables.

PyTorch:

Developed by Facebook, PyTorch is a well-known, open source, lightweight, flexible framework that depends on the Torch library. It utilizes the standardized debuggers like PyCharm and PDB. It has a Python interface and a C++ frontend.

Widely leveraged for natural language processing, PyTorch is used extensively from research prototyping till production implementation. It uses the TorchScript for a smooth shift between the eager approach and graph approach.

It offers different discussions to understand ML better and gives access to many libraries and dynamic graphing feature. There is a good amount of documentation available with easy editing competencies. It can be used by all those who know and understand Python.

Keras:

Considered a faster framework, Keras is competent to deal with huge volumes of data. It is considered a preferred choice for beginners; Python based developers and assists in coding precisely and effectively. There is an inbuilt support for data parallelism.

As a simple to use framework, it can be leveraged for increased complexity computations with deep neural networks. As an extensible tool, Keras encourages swifter investigation and can speed up the training time for models. It is considered ideal for prototyping fundamentals.

It is basically utilized for summarization, classification, speech recognition, translation etc.

Scikit-learn:

Scikit-learn is a Python based free Machine Learning framework that is easily interoperable with many other Python libraries. It uses Cython to develop certain algorithms that focus on enhancing performance. There are many ML models like decision trees, random forests, regression, clustering etc. that can be implemented with Scikit-learn.

It consists of easy and effective tools for predictive analytics. It is accessible to all and has many reusable components. It is built on top of SciPy and allocated under the 3 clause BSD license. Different components of Scikit-learn include supervised / unsupervised learning algorithms, cross validation, toy datasets and feature extraction.

Caffe:

Caffe signifies “Convolutional Architecture for Fast Feature Embedding”. It is a popular deep learning framework that is written in C++. It has a Python interface and allows you to transit between CPU and GPU. It supports different CPU / GPU libraries and hence is considered ideal for image categorization and breakdown.

Considered as one of the swiftest frameworks, Caffe has been used most by startup organizations, multinationals, and academicians in the areas of speech and computer vision. It is apt for mobile platforms and is used most for visual recognition. Developers using Caffe need to know C++ in a preliminary basis.

Apache Mahout:

Created by the Apache Software Foundation, Apache Mahout has its main focus on linear algebra and statistical engines. It empowers its users to utilize their mathematical algorithms in a collaborative manner.

Mathematicians and data scientists form the core user group for this framework. Most of the important algorithms in Apache Mahout make use of Apache Hadoop or Apache Spark.

Apache Spark:

With its foundation as Spark Core, Apache Spark is an open source ML framework that offers programming interface for complete clusters. It focusses on RDD abstraction and is flexible by nature.

Data can be accessed from different data sources – HDFS, Apache Hive / HBase / Cassandra etc. As a popular cluster processing ML structure, It offers DataFrames to execute on independent cluster mode.

Accord .NET:

Written entirely in C#, Accord .NET is a popular ML framework that offers complete focus on areas like neural networks, regression, statistics, clustering etc. It also handles audio / video processing libraries. These libraries exist as the base code as well as different packages.
Considered to be apt for scientific computing in .NET, it is a comprehensive ML framework to create signal processing and statistics apps, computer vision / audition. There is a lot of helpful documentation available to get kick started. It encompasses different areas like statistics, artificial neural networks, image / signal processing, numerical linear algebra etc.

Microsoft Cognitive Toolkit:

A well-known ML framework, Microsoft Cognitive Toolkit (CNTK) is ideal for developing enriched deep learning models. It is used to define neural networks as a series of targeted graphs. This framework utilizes different servers and GPUs offering multi-machine backends.

It can also be used as a library in different C, C++, Python based programs. CNTK is considered ideal for huge dimensional, unstructured, big scaled data operations. It helps developers in merging and viewing different ML models that include deep neural networks and recurrent / convolutional neural networks. There is relatively, a faster learning curve and easy architecture.

MXNet:

MXNet is a preferred choice by many, in the world of ML and deep learning frameworks. It has a good support and scalability for various GPUs, programming languages and facilitates developers to select their own language for developing ML models.

Highly scalable, portable, and fast in nature, MXNet has a customizable model of working and hence is very adaptable. Go, R, Python, Perl, C++, Java etc are some of the languages it supports. It supports DL models like convolutional neural networks and long short-term memory networks.

Gluon:

Gluon has been a current add-on to the popular ML frameworks. It has been known for its speed and ease of use. It tags along a comprehensive group of plug & play neural networks that assist in lessening the complexities involved during implementation.

Gluon consists of a robust API to create different machine learning models. The coding style in Gluon is quite simplistic and transparent. It depends upon MXNet and offers a lot of flexibility to the entire procedure of coding.

It empowers the users to choose from a dynamic neural network and any structure they wish to select from. Because of its simplicity and flexibility, it is easy to be prototyped and showcased.

Firebase ML Kit:

Google came up with a novel idea of Firebase ML Kit – a strong contender in the list of ML frameworks. It needs lesser experience and expertise to embed ML models into mobile applications. An ideal for novice developers, this framework has pre-defined models that help you create minimal coded tasks.

Being a multi-platform provision, Firebase ML Kit offers image labelling, text recognition and object categorization.

Shogun:

A free of cost, open source ML framework – Shogun gels perfectly with C++. This framework has gained popularity in the education and learning industry. There have been algorithms and programs that have been developed for this segment.

Shogun has exhibited compatibility with other important languages – Java, C#, Ruby, Lua, Python, R and more. It has been able to execute huge bulk of data and offer flexible and user driven characteristics.

Apple’s Core ML:

It is Apple itself who developed this framework – Apple’s Core ML. Initially, it was made for iOS, TVOS apps and macOS. It has been a user-friendly framework, easily adaptable by novices. As a thorough platform, it offers a lot of exciting takeaways like NLP, image classification, object tracking and more.

Core ML has been way ahead in providing security and simplicity of use. Data security and privacy play an important role while using this framework. It offers great performance by utilizing the best of CPU and GPU.

Leveraging The Niceties Of Machine Learning, As We Wrap Up

The whole world is enjoying the heavy benefits that Machine Learning as a Service and ML solutions are offering, no matter which industry segment you belong to. The future is bright, the results are paying and there is no looking back! The list of Machine Learning frameworks is increasing. Let us leverage the results that ML frameworks are offering, it sure has a long way to go.

Author: SPEC INDIA

SPEC INDIA, as your single stop IT partner has been successfully implementing a bouquet of diverse solutions and services all over the globe, proving its mettle as a boutique ISO 9001:2015 certified IT solutions organization. With efficient project management practices, international standards to comply, flexible engagement models and superior infrastructure, SPEC INDIA is a customer’s delight. Our skilled technical resources are apt at putting thoughts in a perspective by offering value-added reads for all.


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