Top 7 Machine Learning Frameworks for Web Development
Proving to be a revolutionary technology of our generation, machine learning has its roots in the 1950s. It was in 1959 that Arthur Samuel, a computer scientist working at IBM coined the term machine learning. Interestingly enough, he developed the program to teach computers to play the game of Checkers.
In the 1960s, Alan Turing’s studies further contributed to this field. A more practical application of machine learning was developed in 1967 with the Nearest Neighbour algorithm. It was the first pattern recognition algorithm and was used to help salespeople find the most optimal path to their destinations.
Fast forward to today, machine learning is the fuel behind concepts like self-driving vehicles, natural language processing, and dynamic pricing, to name a few. It is reshaping internet paradigms.
In this article, we will explore how Machine learning is impacting web development. We will start with the relevance of machine learning in web development and then take a look at different frameworks. Machine learning is the future, so knowing more about this fascinating technology can definitely help you as a developer.
Relevance of Machine Learning in Web Development
Employing scalable solutions that can be improvised keeping in line with customer demands has become the need of the hour. To successfully consolidate this shift in web development, it becomes imperative that we use intelligent technologies like machine learning.
To understand the relevance with respect to our everyday life, let us look at one practical use case of machine learning. Today, e-commerce platforms suggest what to buy, on-demand entertainment platforms tell us which movie to watch, and food delivery solutions give us the best meal options.
Personalization of this level is evidence of the monumental role of machine learning in the future of web development.
Moreover, personalization is just one of the many possible applications. Machine learning contributes to web development in several other ways as well, like chatbot development and content generation. In the coming years, we will see machine learning adding many more things in its stride.
Top Seven Machine Learning Frameworks for Web Development
Choosing a machine learning framework for web development requires good research. So much of the choice depends on why a website is looking to incorporate machine learning, what its priorities are, and what the web development frameworks they will be using are. In this section, I will be listing the top seven frameworks for machine learning that are being widely used today and will remain relevant in the future.
The Google Brain team originally developed TensorFlow for their internal use, but in 2015, it was released to the public. It is an open-source software library and supports programming languages like Python, C, C++, Java, and Go.
TensorFlow is used to develop machine learning models for neural networking, natural language processing, speech/text recognition, and forecasting. It’s the brain behind Google Translate services and is also used by Twitter, IBM, and others.
The use of a single high-level API helps to make cleaner code that is easier to read, write, and debug, simplifying computation on one or more CPUs or GPUs. On the whole, it brings machine learning technology to the frontend, giving users real-world experience through web browsers.
Microsoft Cognitive Toolkit
Developed by Microsoft Research, Cognitive Toolkit (CNTK) was released in 2016. It is an open-source deep learning framework written in C++ with interface support for Python. CNTK is mostly used for speech recognition, predictive analysis, and analysis of large unstructured datasets. It supports features like parallel execution and automatic differentiation.
CNTK is scalable and functional, eliminating the need for large, bulky code. It supports various frameworks and tools and is popular owing to its ease of training. Its support for Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is also one of the most noteworthy features of the CNTK framework.
This is an open-source machine learning framework developed by Apache Software Foundation to assist in building complex web applications. Mainly designed for mathematicians, data scientists, and statisticians, Apache Mahout simplifies and improves the efficiency of mathematical algorithm execution. Being a distributed linear algebra framework, Mahout is attributed with exceptional performance.
Mahout is written in Java and Scala and is flexible for cross-platform development. The tool finds extensive application in the development of recommendation-based engines where classification, mining, and grouping of large volumes of data is necessary. As a result, Mahout-based web development is often used where groups need to be classified based on purchasing patterns, product features, and other similar elements.
In 2017, Berkeley Vision and Learning Center released Caffe2, a deep learning framework written in C++ and Python. A flexible and easy framework, Caffe2 requires developers to have just the basic knowledge of C++ to get started with coding.
Extensible code of Caffe2 enhances app performance, and the support to advanced models ensures that app code is uncomplicated. It has Python and Matlab binding and predefined networks that support web development. Further, using pre-trained models, no-code development can be carried out.
The framework often finds applications in creating websites that need to implement image segmentation and classification. Thanks to CNN, it ensures that the development process is fast and efficient.
If a secure, fast, and popular framework for building machine learning models is your requirement, Apache Singa makes a top contender. It was developed by Apache Software Foundation and released in 2015. Initially, it was developed to support an abstraction-based programming model, but its flexible architecture ensures that it can support synchronous, asynchronous, and hybrid approaches.
Singa is a very versatile framework that finds application in different domains and scenarios. Several health care solutions use Singa to analyze electronic medical record data. It is also used in image recognition and natural language processing. Currently, the development of Singa-Lite and Singa-Easy is underway, and these enhancements will ensure that machine learning becomes easier and more accessible.
PyTorch is a machine learning framework that received instant recognition soon after its release in 2016. Under Facebook’s AI group, PyTorch was developed as an open-source machine learning library for Python based on Torch (another machine learning framework). Its algorithms are developed in line with Python control flow; hence, Python developers find it easy to use.
PyTorch is mostly used in natural language processing. With its high performance, efficient GPU utilization, and premade data training models, PyTorch is considered to be one of the best frameworks for machine learning.
Developed by a Japanese company in partnership with IBM, Intel, Microsoft, and Nvidia, Chainer is an open-source deep learning framework built on Python’s Numpy and CuPy libraries. It is mostly used in speech recognition, machine translation, and sentiment analysis.
Chainer uses a Define-by-Run approach in which the connections in a network are not predetermined but dynamically done during the actual training. Its fast performance and easy debugging make Chainer the go-to framework for developing large-scale systems.
Machine Learning Is the Future in Web Development
It’s an undeniable fact of our current digital reality that machine learning is prospering. This is thanks to the huge amount of data available to train the machine learning models, as well as due to machine learning being more affordable nowadays, with the hardware, storage requirements, and other things being available more economically.
As we saw in this post, machine learning brings tremendous benefits. It helps analyze customer activity and understand their behavior to identify their expectations and improve user experience. Moreover, it employs automation to improve data mining, detects trends more efficiently, and automates the response according to the observations.
Machine learning makes the website browsing experience personalized, unique, and relevant, helping customers reach their goals faster and better, thanks to streamlined marketing strategies as well as fact-focused and result-oriented improvements. Additionally, machine learning creates strong security checking processes and secures websites from evolving security threats.
It should come as no surprise that the power of machine learning combined with web development will change the future of the digital world. The web development services will embed machine learning algorithms in the development process to meet the future expectations of users and businesses. Unlimited possibilities are at hand for developers with this groundbreaking technology and the impacts will undoubtedly be transformative!