If you are a tech geek like me, you might follow tech news closely. If that is the case, then you might have noticed the terms like “machine learning” and “artificial intelligence” making the headlines frequently and for good reason. They are two of the hottest topics in technology right now and offer immense potential to transform every facet of our lives.
Think of any industry and you might find artificial intelligence or machine learning at work in some capacity. Whether it is healthcare, manufacturing, retail construction or transportation, you will find AI and machine learning everywhere. One industry that has not been affected by AI and machine learning yet is web & app development. Thankfully, that is about to change as well.
Machine learning takes advantage of its algorithms to give machines learning capabilities. This means that you don’t have to program than like programming robots. With unrivaled data analysis capabilities, it makes creation of analytical model simpler and easier. That is why machine learning framework can transform web development as we know it today.
With the advancement of machine learning, we are seeing the emergence of machine learning frameworks and tools for web and app developers that will make development a breeze. In this article, you will learn about five amazing machine learning frameworks that you wished you know about earlier.
1.TensorFlow
TensorFlow is by far the most popular machine learning framework out there. Predominantly used for Java development, this open source library that harnesses the power of data flow graphs for computation purposes. What makes TensorFlow stand out from rest of the machine learning framework is its ability to perform one or more CPUs with a single application programming interface (API).
Web development companies can use this to their advantage and can use any device whether it is a server, desktop computer or a mobile device for this purpose. Mathematical operations are represented with a node on the graph while multi dimensional data sets between the nodes are depicted by edges on the graph. The ease at which you can create and deploy different models and the ability to run powerful experiments for research purposes gives it a clear edge over its competitors.
2.Microsoft Cognitive Toolkit
Tech giants like Google, Microsoft and Amazon are betting big on AI and machine learning. That is why they are investing millions of dollars into these technologies. Microsoft Cognitive Toolkit is an open source deep learning toolkit that focuses on training algorithms and machines to learn like a human brain. This machine learning toolkit allows you to make the most of various machine learning models such as feed forwards DNN, recurrent neural networks or more.
There is no denying that this toolkit is primarily designed to sift through large volumes of unstructured data but that does not mean that it cannot do other things. It’s easy to use architecture and its ability to customize it to your heart content makes it head and shoulders above the rest. Users can select their own algorithms, network and even their own parameters based on their needs. Microsoft Cognitive toolkit can also support multi GPU and multi machine architecture, which other machine learning framework does not. With backing from Microsoft, you can be rest assured that you are in safe hands.
3.Apache Mahout
Although, not as widely known as TensorFlow, Apache Mahout is not far behind either when it comes to popularity. Designed with the needs of data scientists, statistician and mathematician in mind, this open source framework from Apache allow you to run algorithms and calculations faster. The main focus of Mahout is on classification, filtering and grouping.
Want to create your own mathematical calculations? You can also do that with Apache Mahout that too in an interactive platform. For those who are interested in statistics and algebra calculations, Mahout has a separate version called Mahout Samsara. By harnessing the power of big data by using Apache’s own Hadoop architecture but does not restrict users in implementation.
4.Caffe
Designed with speed and modularity in mind, Caffe is a deep learning framework targeted towards Java developers. It’s open architecture help developers personalize their applications and pave the way for innovation. Users can even switch between different CPUs and GPUs, thanks to the customization options. All they have to do is to configure an indicator and they are good to go.
The extensibility of code led to the rapid growth of this tool. It’s amazing speed makes it a great choice for research and industrial use. For those who don’t want to code, they can look at the Model Zoo, where they can find pre trained models ready for implementation. It is one of the best options for creating applications and artificial and computer vision but it will fall behind other players when it comes to wide scale adoption in other industries.
5.Apache Singa
If you are looking for a scalable deep learning solution that offers you the flexibility to grow then, look no further than Apache’s Singa. Written in C++, Python and Java and developed by National University of Singapore, this machine learning framework offer flexible architecture to cater to your distributed training needs. It is great at natural language processing and image recognition but its ability to run of wide range of hardware makes it a suitable choice for other applications as well.
It is still far behind its other counterparts when it comes to availability of different models as users are limited to only a handful of models. It only supports traditional machine learning models such as logistic regression which is a big downside especially for hose users who want to try modern machine learning models and take advantage of their capabilities.
Do you use machine learning frameworks for web development and mobile app development? If yes, then which machine learning framework do you use for web development? Feel free to share it with us in the comments section below.
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