With digitalization progressing at an increasingly fast pace, businesses are adopting machine learning and artificial intelligence in their everyday operations.
As technology advances in 2020, a variety of machine learning frameworks come to light. Knowing which software fits your needs could mean the difference between successful business investment and a failed venture.
Here, we’ll discuss what machine learning and the best ML frameworks in 2020 are:
What is Machine Learning?
Machine learning is a growing obsession that concerns data analysis and data science. The ever increasing demand for AI and ML means mobile app developers need to step up their game and learn the difference in types of machine learning.
Machine Learning is a sub-domain of artificial intelligence that helps businesses examine the data, learn, and adapt, then make decisions accordingly. It assists in system processing, automates repetitive admin tasks, and more.
Furthermore, Deep Learning is a subsidiary of machine learning. It offers efficiency and accuracy that exceed past the profound cognitive abilities of the human brain. The demand for higher proficiency urges data scientists to develop Deep Learning platforms that are intuitive and innovative boosting company productivity.
The best Deep Learning frameworks are either an interface or tools that help developers construct Deep Learning models with ease. Additionally, it eliminates the need to understand the details of the ML/DL algorithms of the Machine Learning and Deep Learning platforms.
Machine Learning can be divided further into three types:
Supervised machine learning comprises predicting and sorting through data based on past inputs. For example, email software like Gmail and Yahoo filter spam emails via machine learning algorithms.
Facebook is an excellent example of unsupervised machine learning. The ML frameworks of the social platform detect the hidden patterns and recognize facial features in pictures and your friend list to suggest recommendations.
Reinforcement Machine learning is primarily concerned with boosting efficiency.
10 Machine Learning Frameworks in 2020
Machine Learning Frameworks are tools for developers to understand and design machine learning models. It helps create powerful AI software without diving into the technicalities of the algorithms.
Machine Learning helps speed up data processing and increase the accuracy of results. Here are some of the best Machine Learning tools in 2020 that developers should consider when integrating AI into mobile apps or other software:
Developers can run the framework on CPUs and GPUs. One of the advantages of TensorFlow is that it can be used in two ways, Script Tags or by installation via NPM. Additionally, it can assist in human pose estimation.
The drawback, however, is that it is challenging to learn and understand for beginner developers.
Google Cloud ML Engine
Cloud ML Engine is one of the best Deep Learning frameworks that offer training for amateurs, among other services for building machine learning models. It helps developers and data scientists with forecasting in various fields and domains.
The Machine Learning framework is the optimum choice for ML model training and building, plus deep learning and predictive modeling. Furthermore, training and predictive services can be used independently or together. Enterprises most often use the framework to speed up email responses to customers and detecting clouds in a satellite image.
Apache Mahout is one Deep Learning platform operating on a distributed linear algebra framework to scribe and implement ML algorithms. The scalable algorithms are for classification, clustering, and batch-based collaborative filtering.
Developed by Apache Software Foundation, it is also an open-source, free platform that uses the MapReduce paradigm and runs on top of Apache Hadoop. Furthermore, as one of the best machine learning tools, Mahout offers matrix and vector libraries, distributed fitness functions for programming, and more.
Benefits of Apache Mahout include the fact it works for extensive data sets, is simple to operate and extensible. On the other hand, it requires detailed documentation for ease of use, plus some of the algorithms are missing.
Shogun is another open-source machine learning framework compatible with the C++ programming language. It is a free platform that developers can use to design algorithms and data structures, primarily for ML problems in education and research.
Gunnar Raetsch and Soeren Sonnenburg designed Shogun in 1999 to support vector machines for classifications and regression problems, plus large-scale learning. It allows developers to connect with other machine learning libraries, including LibLinear, LibSVM, SVMLight, LibOCAS, and more.
Furthermore, Shogun also supports other languages and frameworks, including R, Python, Java, Octave, C#, Ruby, MatLab, Lua, and more. Among the perks of the ML framework is that it helps implementing Hidden Markov models, can process extensive data sets, convenient to use, and filled with flexible features and functionalities.
Sci-Kit Learn is a machine learning framework that supports development in Python with a library for Python programming language.
It is one of the best ML frameworks for data mining and data analysis. Sci-Kit Learn also supports designing models and algorithms for classifications, regression, clustering, pre-processing, Dimensional reduction, and Model selection.
Sci-Kit Learn is a free platform that is easy to understand for even amateur developers with its detailed documentation readily available. Additionally, it also allows changing of the pre-set parameters for algorithms when in use. The framework is preferred for unsupervised and administrative calculations.
PyTorch or TORCH
PyTorch or Python ML framework, based on Torch and Caffe2, supports Lua programming language for the user interface. The platform is an ML library used by Facebook, IBM, Idiap Research Institute, and Yandex.
With tons of options for optimizing algorithms, Torch is used to design neural networks using Autograd Module and natural language processing. It is an open-source, Deep Learning platform that supports cloud software development and provides multiple features, including tools, libraries, and distributed training.
The Torch is the best choice for designing computational graphs and its hybrid front-end that simplifies its usability. The platform was designed for integration into Python and used with libraries like Numba and Cython.
Another open-source Machine Learning framework in the list is H2O. It is an AI tool designed to assist businesses in decision-making processes based on the data available. The platform is primarily used for risk and fraud analysis, insurance analytics, predictive modeling, healthcare, advertising, and customer intelligence.
Microsoft Cognitive Toolkit (CNTK)
A Machine Learning platform from Microsoft, CNTK, describes neural networks as a sequence of computational steps through directed graphs. It is also an open-source framework designed with algorithms in C++ programming language and production readers.
CNTK is best when dealing with large-scale, multi-dimensional, or sparse data sets from C++, Python, and BrainScript. It helps developers in merging various ML model types, including recurrent networks, feed-forward deep neural networks, and convolutional neural networks.
Amazon adopted the Apache MXNet Deep Learning framework as its primary Machine Learning tool on AWS. The platform was designed to be distributed on a cloud infrastructure via a parameter server. It can scale across multiple GPUs and servers.
Apple’s Core ML
Core ML focuses on developing machine learning models compatible with MacOS, iOS, and TVOS applications. It is convenient to use for even beginner developers.
The ML platform allows image classification, sentence classification, natural language processing, barcode detection, Gameplay Kit, and object tracking.
One significant advantage the platform offers is that, with its low-level tech stack, it can leverage CPUs and GPUs for phenomenal performance. Furthermore, it helps ensure the privacy and functionality of the app, even when offline.
Which Framework is the Best for You?
I hope the article provides a sufficient Deep Learning framework comparison to help you understand the different types available for designing ML models.
Your choice of machine learning frameworks depends entirely on the algorithm requirements, your expertise, and the client’s budget. Our list, however, contains ML tools that are free to use. Our mobile app experts at Cubix are ready to offer more insights and support for Machine Learning frameworks. So, share your idea and explore the applications of ML for your company.