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Top 8 Open Source AI Framework

Open Source AI Framework – Top 8 Sources

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Open Source AI Framework – Introduction

It’s a common misconception that an AI architecture based on neural networks needs to use programming languages. An AI application’s results are controlled by the model architecture rather than a particular language.

Natural Language Processing (NLP), computer vision, and image processing are, unsurprisingly, the three primary forces behind AI.

The majority of well-liked neural processing frameworks are offered as cloud services, including Google’s Tensorflow. Google’s Tensorflow was the most widely used machine learning framework in 2018, both in terms of installs and downloads, according to my data analysis from Stack Overflow.

Now let’s examine the best open-source AI frameworks.

Open Source AI Framework
Open Source AI Framework

Open Source AI Framework are:

1. TensorFlow: It is quite simple to set up and expand Google TensorFlow, an open-source software platform for creating and using machine learning neural networks. It is the most well-liked deep learning framework, with the second-highest percentage of open source repositories and the most GitHub ratings.

Tensorflow is undoubtedly the framework that novice programmers can work with most easily. However, certain experts in neural processing may be daunted by the sheer quantity of tools and features, which might be practically incomprehensible to seasoned coders. We will now learn more about RNN, the next AI framework.

2. RNN: An exceptionally adaptable and user-friendly interface characterizes RNN, a new framework for supervised learning. Additionally, it is appropriate for creating “deep learning” algorithms that can be used to discern between “like” and “dislike” in data sets. AI Framework

RNN is the second-most popular deep learning framework for processing neural and linguistic data. The user community has been tremendously helpful and engaged, and the project is still actively being developed. According to neural processing experts, it’s not the best solution for general ML coding because of the extra layers of abstraction. RNN is too difficult to master but is a lot of fun to explore with, according to neural processing expert Joe Callaghan, who also likened RNN to WATM. (Referring to Stack Overflow)

Theano is the next AI framework.

3. Theano: It is a popular open-source deep learning package for Python that is also well-liked in the fields of neural processing and data science. It is well known for making the development of complex neural networks simpler by reducing the components of the neural network (such as the layers and hidden layers). On graphics processing units (GPUs), it is often used for building and refining AI models, and Facebook has used it for both the creation and the distribution of AI apps.

A library of algorithms that operate on data frames using neural networks is included with Theano. The most well-liked AI framework currently employed by programmers that utilise Theano or Tensorflow is compatible with Python, C++, Java, Julia, Scala, and Tensorflow. Any platform can be used with Theano, but the majority of Theano developers utilise Tensorflow and Tensorboard.

Theano is a deep learning framework with a large collection of sophisticated algorithms. It is used to train computer programmes that execute tasks including speech recognition, object identification, language translation, and image classification. Theano’s library of well-known machine learning algorithms is the largest and can be coupled with Tensorflow with ease.

The majority of deep learning applications employ Theano and Tensorflow. They are not, however, the ideal option for NLP. AI Framework

4. PyTorch: An optimized Python framework for creating machine learning algorithms is called PyTorch. It is frequently used by researchers for study, but it is also well-liked by developers that utilise TensorFlow.

For building systems, big or small, Medium is a free, open-source Python framework. Because it provides the most thorough interface to hardware accelerators and a nice API, the developers claim that it is the most “intuitive” framework for creating systems. When working with GPUs, it is known for having a poor response time. AI Framework

Because of Torch’s exceptional versatility, developers may use it to train, test, and deploy deep learning and natural language processing (NLP) systems. It doesn’t appear to be used as frequently as other, more established frameworks, and it can be difficult to set up and maintain.

A Python package for creating machine learning models is called Parsey McParseface. Its interface is comparable to Theano’s, but it supports a larger selection of APIs and a wider variety of training data types. The following AI framework will be examined now.

Open Source AI Framework
PyTorch – AI Framework

5. Caffe2: Caffe2 is not a conventional framework for AI training, and this must be understood. It is actually an inference engine that has been taught using neural networks. In comparison to Caffe, Caffe2’s ultimate objective is to deliver the best results in the most effective way possible.

Using the PyTorch framework, Caffe2 is a strong open-source toolkit that makes it simple to build deep learning models. We can quickly develop scalable models without using the same calculations as traditional models. Because of this, Caffe2 allows us to get the most out of our machines and maximise their efficiency.

Since it is a Python library, you don’t need to worry about using any other libraries or outside programmes because you can rely on it to provide the entire structure for your project.

6. The MxNet: With the use of a function called “forgetful backdrop,” which is especially helpful for recurrent networks with extraordinarily lengthy sequences, it enables the trade-off of computation time for memory. When creating this application, scalability was the main focus (easy-to-use support for multi-GPU and multi-machine training). There are a lot of fascinating new features, such the simplicity with which high-level languages can be used to create custom layers. AI Framework

7. Keras: A high-level AI API called Keras can be used in conjunction with Theano, Microsoft Cognitive Toolkit, and TensorFlow. Keras is the preferred choice for quickly developing new apps because of its simplicity of usage and emphasis on the developer experience. Keras has been incorporated into the fundamental offerings of numerous companies, including smaller businesses and well-known names like Netflix, Uber, and Yelp. In order to predict client turnover, which is essential for a subscription-based business, Netflix, for instance, has used deep learning. AI Framework

8. Flux. Jl: Unlike high-level application programming interfaces from libraries like PyTorch’s or TensorFlow’s Keras, it adopts a different strategy. Supporting coding patterns that closely resemble the equations you may see in a research paper detailing a new ML technique is preferable to sticking to the roots of its mathematical and scientific computing. In addition to DiffEqFlux.jl, which we previously mentioned, Flux.jl is used in numerous other Julia language machine learning applications.

The best place to start is definitely with Zygote.jl, a Flux-based library for advanced automatic differentiation, if you’re a curious novice searching for something that most closely resembles Autograd or JAX from Python. AI Framework

Open Source AI Framework
Best Neural Processing Framework For Beginners

Best Neural Processing Framework For Beginners:

Many developers are unaware of the numerous prebuilt plugins and libraries that can boost RNN and Tensorflow, despite the fact that some sophisticated deep learning frameworks support neural processing. Products like Microsoft Azure ML and Google Cloud Machine Learning have these frameworks built into them.

  • Mylica is a Python framework that can be readily customised to meet the demands of various applications to conduct neural processing, reinforcement learning, and reinforcement learning.
  • Karos is a framework for reinforcement learning that is open-source and comes with a collection of algorithms for it. On GPUs, it is renowned for using Tensorflow compatibility to train reinforcement learning systems.
  • A Python module called Trainedata is used to build massive reinforcement learning systems. The algorithms in the library can be used with Python 2.7, 3.4, and 4.x.
  • An open-source deep learning framework that allows reinforcement learning is called Vowpal Wabbit. Vowpal Wabbit is designed to hasten neural network training in reinforcement learning systems.

Python has emerged as the most popular language for NLP development. Although useful for other applications, most of these frameworks and libraries are optimised for running on the Python Virtual Machine, which provides highly efficient performance for processing tensor data. It is a very convenient framework for the development of neural network applications.


Deep learning and neural networks are becoming more popular in some fields, including computer vision, health care, and cyber-security. Machine learning in this emerging field has led to many new applications. As data sources increase, data scientists’ demand is likely to increase significantly, leading to more demand for neural processing skills.

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