These 5 AI open source technologies will take your machine learning to the next level.
The software industry nowadays is moving towards machine intelligence machine learning. Machine learning has become necessary in every sector as a way of making machines intelligent. Now in a simpler way,
“Machine Learning is a set of algorithms that parse data learn from them and then apply what they have learnt to make intelligent decisions.”
How AI is Transforming Businesses and World?
You guys must be aware of the buzzwords going on these days which are Artificial Intelligence and deep learning right and 2015 was a time when we actually observed some of the biggest evolutions in the industry of AI and deep learning.
The way the technologies of Artificial Intelligence have changed the global world in every aspect. From communication to transportation, business to entertainment, AI has transformed everything in every aspect.
Because of these speedy improvements, extensive volumes of talent and resources are committed to stimulating the fullness of the technologies.
Deep learning is gaining much popularity due to its supremacy in terms of accuracy when trained with huge amount of data. Now whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills, picking the right AI Open Source technology to learn is the essential first step towards reaching your goal.
So, in this article, we are going to discuss 5 AI Open Source Technologies we should know about to give new heights of success to machine learning projects. Let’s get started the article.
TensorFlow (which is a Google’s open-source framework) introduced in the year of 2015. TensorFlow is nothing but a blend of libraries, resources, and tools for dataflow programming across a range of tasks. TensorFlow is basically the framework that provides both high and low APIs. If we talk about speed performance, then it has pretty awesome speed to complete desired projects within the time. It is also a symbolic math library and is used for machine learning applications such as neural networks.
Corporations like eBay, Airbnb, Intel, Twitter, DropBox, and others have complete faith in TensorFlow because these giant folks use TensorFlow for their Machine Learning Projects. The functionalities like easy to use and easy to deploy makes it worthy among its competitors. Isn’t enough for you to take TensorFlow for your Machine Learning Projects?
TensorFlow is available in all most major programming languages
It was also introduced in the same year when TensorFlow was released, and it was 2015. Keras is basically an open source neural network library written in Python. Keras is a high level API, and it is capable of running on top of TensorFlow, Microsoft cognitive toolkit, or Tiano.
It has gained popularity for its ease of use and syntactic simplicity facilitating fast development. The architecture of Keras is simpler, concise, and more readable than Pytorch. It is simple and easy to use which is why most of the beginners prefer to use Keras as compared to TensorFlow and other AI open source Frameworks. It is designed to enable fast experimentation with deep neural networks. It also focuses on being user-friendly modular and extensible.
How Netflix is using Keras?
Many giant corporations like Uber, Netflix, Yelp, and also many startups are using Keras for their core products and services. Let’s take a look at Netflix that how Netflix is using Keras? Netflix is using deep learning to forecast client churn, which is an essential as a subscription-based business. Netflix has a bulk of user data and can identify users they suspect will cancel their service to give them offers and some discounts.
The date of Year of Scikit-Learn is 2007. Yes, this year gave a remarkable AI open source technology to the world. Scikit-learn is a Python-based open-source machine learning library dedicated to make machine learning tasks easy.
It is considered as one of the most productive machine learning library in Python. The Scikit learn is integrated with a lot of inventive features, options, and tools like classification, dimensionality reduction, clustering, and regression for statistical modeling and machine learning.
Scikit-learn AI open source framework is created with the help of Matplotlib, NumPy, and SciPy to personalize the machine learning projects.
4. Microsoft Cognitive Toolkit
Let’s have a look at high-level introduction to Microsoft’s cognitive toolkit formerly known as CNTK. It is a state-of-the-art production-ready toolkit for deep learning. Microsoft cognitive toolkit CNTK is designed around ease-of-use focusing on the problem a user needs to solve and not having to worry about how advanced users can certainly deep dive into the toolkit if they want to the toolkit is designed to be hugely performant on large production scale data.
The toolkit is designed to be extensible and the latest v2 Python API release lengths even greater flexibility one can provide one’s own Python functions and additional interoperability.
With NumPy arrays, CNTK embraces fully open development framework that is there is no separate code branch internal to Microsoft. The toolkit is tested in production setting for accuracy efficiency and scalability in a multi GPU multi server environment.
The toolkit is also designed to bring lego-like into inter probability and extensibility allowing for expression of arbitrary numeral networks by composing simple building blocks into complex computational networks.
Microsoft Cognitive Toolkit can handle the data from C++, Python, or BrainScript, ability to deliver effective resource usage with Microsoft Azure, & interoperation with NumPy.
Originally introduced in the year of 2007, Theano is an open-source library of Python that enables users to efficiently fashion several machine learning models. As it is considered one of the oldest libraries in the town, it is recognized as a business standard that has motivated developments in deep learning.
At its essence, it allows you to clarify the process of optimizing, defining, and evaluating mathematical expressions.
Theano is able of taking your structures & remodelling them into very effective code that combines with NumPy, effective native libraries such as BLAS, and native code (C++).
Moreover, it is optimized for GPUs, gives adequate symbolic differentiation, and appears with extensive code-testing skills.
Final Words for AI Open Source Technologies
So, These are 5 AI open source technologies. Before beginning to develop a machine learning application, choosing one AI open source technology from the several options out there can be a challenging job. Consequently, it’s necessary to assess various options before going to decide a final decision.
Moreover, learning how different machine learning technologies work can help you to take a better decision.