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.
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.
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.
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.
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.
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.
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.