Looking for some of the best AI frameworks and tools to develop artificial intelligence apps? Herein in this blog, we have listed 7 best artificial intelligence frameworks and tools that we have selected on the basis of usage, popularity, features and functionalities.
Artificial intelligence has been around for a very long time but it was considered to be extremely difficult. It was quite common for researchers and developers to shy away from studying or using it.
Fortunately, this all this changing. In recent years, AI frameworks and Tools for AI have made it much friendlier for the IT sector. This is why we found it the right time to tell you AI enthusiasts about the best AI sourcing tools, frameworks, and libraries.
We have made sort of an AI framework comparison of the best AI tools in the list below. We have talked about the best AI frameworks as per our experience in the field.
We do understand that every data scientist has a unique requirement for his project and this is why this list will be helpful for you. You can choose the Artificial Intelligence software tools that work best for you.
Note: The list is not in chronological order of the efficiency of frameworks. These are simply the top AI tools that have their own place in the constantly changing world of AI.
7 Best AI Frameworks And Tools To Choose From
Even if you’re someone who just started to learn about developing apps and software using AI, you must have heard this name. TensorFlow is one of the most popular AI frameworks or libraries, and the best part is that it is open source.
Being backed by Google doesn’t hurt and has a client-base that includes the likes of Airbnb, eBay, Dropbox, and Coca-Cola. In fact, it was developed by Google’s AI department and now has become a favorite AI tool of the top tech giants such as AMD, SAP, Google, Intel, Nvidia, and others.
It is perfectly suited for complex numerical computations of high volumes using data-flow graphs and machine learning. This framework has an architecture that allows computation on any CPU or GPU. it is compatible with desktop, server, or even mobile devices.
It supports many classification and regression algorithms, and deep learning and neural networks.
- Programming language: Uses an easy-to-learn language Python, C++, CUDA
- Pros: Keeps code lean and development efficient due to simplifications and abstractions
- Cons: It’s slow, as Python is not the fastest of languages and lacks pre-trained models
2. Microsoft CNTK
How could Microsoft stay behind Google? CNTK is how Microsoft responded to SensorTower.
Microsoft Cognitive Toolkit or CNTK is an open-source toolkit for deep-learning. It uses a directed graph to describe neural networks as a series of computational steps. It designed to support robust, commercial-grade datasets and algorithms.
This library that enhances the maintenance of separating computation networks, providing learning algorithms and model descriptions. It can also build, train, and run many types of deep neural networks.
It has its own model description language called BrainScript and can be used as a standalone machine-learning tool. Not only that, but CNTK can also be included with Python, C++ any .NET language including C#, or Java (model evaluation functionality).
This library can be used in apps like the ones that can predict house prices or such. Without sacrificing a quality degree of speed and accuracy, it offers efficient scalability from a single CPU to GPUs to multiple machines. It serves big clients like Skype, Cortana, and Bing.
- Programing languages: C++, C#, Java, and Python
- Pros: It is very flexible and allows for distributed training
- Cons: Implemented in Network Description Language and lacks visualization
Keras is an open-source AI tool- a neural network library written in Python just like TensorFlow, CNTK but unlike them, it is not meant to be an end-to-end machine learning framework.
Instead, it operates as a user-friendly, easily extensible interface. This provides a high level of abstraction, making the configuration of neural networks easy regardless of the framework it is sitting on.
New modules are easy to add while neural layers, cost functions, and other standalone modules can be combined with few restrictions.
This Python deep learning library is a high-level neural network API that can run on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano and supports modularity and total expressiveness.
- Programming language: Python
- Pros: Runs seamlessly on both CPU and GPU
- Cons: It can’t be efficiently used as an independent framework
Theano gives strong competition to TensorFlow. This artificial intelligence tool is a Python library that allows defining, optimizing and evaluating mathematical expressions, that involves multi-dimensional arrays efficiently. It particularly evaluates matrix-valued expressions.
This AI library uses GPU transparently for carrying out data-intensive computations instead of a CPU which results in high efficiency in its operations. In fact, Theano has also been able to top C on a CPU by a significant degree.
Large-scale computationally intensive operations have been powered by Theano for the same reason.
For this reason, Theano has been used in powering large-scale computationally intensive operations for about a decade.
Theano pairs elements of a computer algebra system (CAS) with elements of an optimizing compiler. This is how complicated mathematical expressions that require repeated and fast evaluation get an ideal environment.
Even though new development for Theano has ceased in 2017, it is still a very powerful library. It is a very powerful and efficient platform for deep learning.
- Programming language: Python
- Pros: Properly optimized for CPU and GPU and efficient for numerical tasks
- Cons: A bit buggy on AWS and needs to be used with other libraries to gain a high level of abstraction
5. Sci-kit Learn
Sci-kit learn is for Machine learning in Python
Scikit-learn is one of the Artificial Intelligence open-source tools, a commercially usable AI framework. It is a Python library that supports both supervised and unsupervised machine learning.
This is one of the multipurpose Ai development tools, it supports classification, regression, and clustering algorithms, and also, dimensionality reduction, model selection, and preprocessing.
Data scientists can quickly access resources on anything from multiclass and multilabel algorithms to covariance estimation using the extensive user guide offered by sci-kit learn.
Sci-kit learn comes with features such as cross-validation, supervised and unsupervised learning algorithms.
- Programming language: Python, CUDA, C, C++
- Pros: Many main algorithms are available
- Cons: Not very efficient with GPU
Berkeley AI developed Research Caffe is a deep learning framework in collaboration with community contributors.
Expression, speed, and modularity are prioritized in its framework. It has a strong architecture that supports configuration-defined models and optimization without hard coding. It is also capable of switching between CPU and GPU.
Caffe is suitable for research experiments and industry deployments because it can process over 60M images per day with a single NVIDIA K40 GPU.
This AI framework is adaptable with C++, CUDA with Command line, Python, and MATLAB interfaces. Building a convolutional neural network (CNN) for image classification with Caffe is very easy.
- Programming language: C++
- Pros: Allows for the training of models without writing code
- Cons: Bad for recurrent networks and not great with new architectures
Torch is a scientific computing framework for scientific and numerical operations. It builds algorithms with speed, flexibility, and simplicity.
Torch claims to put GPUs first and is a Tensor Library that’s similar to NumPy. It is wrapped in LuaJIT and has an underlying implementation of C/CUDA.
It has improved efficiency and makes for easier deep learning research by providing a large number of algorithms. Torch users are provided with easy to use libraries while enabling flexible implementation of complex neural network topologies.
With a powerful N-dimensional array, it helps with operations such as slicing and indexing. It also offers linear algebra routines and neural network models.
- Programing language: Lua, LuaJIT, C, CUDA and C++
- Pros: Lots of pre-trained models available and very flexible
- Cons: Documentation is quite unclear and Lua is not a very popular language
These were just 7 of the best AI frameworks according to us and we understand that your choice and preferences may differ from us. You might use other AI libraries altogether.
We will keep updating the list with our experience and research in this segment. If we have missed any important information about any of the above AI tools, let us know in the comments below. We are always striving to improve.
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