6 Open Source AI Frameworks

As businesses accumulate vast amounts of data and seek the right technology to evaluate and use it, artificial intelligence (AI) is gradually becoming more popular. As a result, Gartner predicts that by 2021, AI will be at the heart of 80% of emerging technologies.

Marketers need to start thinking about how they can use predictive analytics, machine learning, and other data sciences to form a more data-driven marketing strategy now that the trend toward predictive analytics, machine learning, and other data sciences is well underway. With that in mind, we asked AI specialists why marketing leaders should start thinking about AI, as well as some of the best open source AI frameworks to keep an eye on.

How Artificial Intelligence (AI) is Changing Business

Artificial intelligence (AI) is changing nearly every aspect of our life at a rapid pace. We appear to be becoming more and more addicted to them, from how we communicate to the modes of transportation we utilize.

As a result of these quick developments, enormous quantities of expertise and money are devoted to speeding up the development of technology.

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Artificial Intelligence Frameworks & Tools

We as a species have always tried to develop things to help us in our day-to-day work from the dawn of history. From stone tools to modern-day machinery, to tools for developing programmes to help us in our daily lives, we’ve come a long way. The following are a few of the most essential open source artificial intelligence frameworks and tools:

1. TensorFlow


TensorFlow, a Google open source ai frameworks, provides an ecosystem of tools, libraries, and resources that are utilized by several well-known firms, including Airbnb, eBay, Dropbox, and others. TensorFlow promises to speed development by simplifying and abstracting the complexity of machine learning algorithms. Developers and data scientists can easily construct neural networks and other machine learning models to utilize data using visual models and flowgraphs. TensorFlow is being used by Airbnb to classify apartment listing images to guarantee that they correctly reflect a specific room.

2. Theano


Theano is an open source Python library that makes it simple to create various machine learning models. It was first released in 2007. It is considered an industry standard that has spurred advancements in deep learning because it is one of the oldest libraries.

Its main function is to make the process of defining, optimizing, and evaluating mathematical expressions easier.

Theano can take your structures and turn them into extremely efficient code that works with NumPy, efficient native libraries like BLAS, and native code (C++).

Additionally, it is GPU-optimized, has efficient symbolic differentiation, and has significant code-testing facilities.

3. Keras


Keras is for you if you prefer the Python method of doing things. It’s a high-level neural network library that uses TensorFlow or Theano as its backend.

Keras is an open source software library that was first launched in 2015 to make the development of deep learning models easier. It’s developed in Python and can run on top of TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano, among other AI frameworks.

Keras is well-known for its simplicity of use, modularity, and flexibility. If you require a machine learning opensource framework that allows for quick prototyping, supports both convolutional and recurrent networks, and performs well on both CPUs and GPUs, this is the library for you (graphics processing units).

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4. Scikit-learn


Scikit-learn is an open source machine learning package that was first released in 2007. This classic Python framework includes a variety of machine learning models such as classification, regression, clustering, and dimensionality reduction.

Scikit-learn is a data mining and data analysis framework based on three other open source projects: Matplotlib, NumPy, and SciPy. It provides a large number of computations for common AI and data mining tasks, such as bunching, relapsing, and order. Even complex tasks like altering data, determining features, and using ensemble methods may be completed with a few lines. 

Scikit-learn is a more-than-adequate tool to work with if you’re just getting started with machine learning until you start implementing increasingly sophisticated computations.

5. Auto ML

Auto ML

Auto ML is arguably the most powerful and latest addition to the arsenal of tools accessible to a machine learning engineer out of all the tools and libraries described above.

Optimizations are critical in machine learning tasks, as mentioned in the introduction. While the rewards are many, selecting the best hyperparameters is no simple feat. This is especially true in black boxes like neural networks, where identifying what matters gets increasingly difficult as the network’s depth grows.

As a result, we’ve entered a new world of meta, in which software assists in the development of software. Many machine learning developers utilize the AutoML package to improve their models.

Apart from the obvious time savings, this may also be quite beneficial for someone who does not have a lot of machine learning knowledge and hence lacks the intuition or previous experience to make specific hyperparameter adjustments on their own.

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6. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit (formerly known as CNTK) is an AI solution that may help you take your machine learning projects to the next level. It was first launched in 2016.

Microsoft says that the open source ai framework is capable of “training deep learning algorithms to function like the human brain.”

Highly optimized components capable of processing data from Python, C++, or BrainScript, ability to offer optimal resource utilization, simplicity of connection with Microsoft Azure, and interoperation with NumPy are just a few of the key characteristics of the Microsoft Cognitive Toolkit.

Wrapping Up

The Artificial Intelligence Frameworks & Tools blog has now come to a close. These are some of the tools that data scientists and engineers may use to address real-world issues, therefore improving and strengthening the underlying architecture.

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