Artificial Intelligence is the future of the field of computing. And more and more programmers are getting acquainted with this science, seeing the increasing need for Artificial Intelligence technologies. I hope this guide will help you to get inspired and start learning more about this growing field.
Here are some statistics: (Reference: https://www.statista.com)
- Service segment revenue of cognitive and AI systems market 2019 – 12.7bn USD
- AI business operations global revenue 2023 – 10.8bn USD
- Natural language processing (NLP) market size worldwide 2025 – 43.3bn USD
- Amount of funding in AI startups worldwide in 2019 – 24B USD
And here is a nice graph showing how many AI startups acquired by high tech companies:
Introduction
So let’s say you decided to practice and develop yourself in this field. Today we will see how software engineers can apply deep learning and Artificial Intelligence into their programming work. The first thing that we must know is how it can be applicable, and here is the question I asked myself and researched: “What are the most useful frameworks/ libraries for using?”
This is just what we’re going to address today in this article: I gathered the most popular 5 Artificial Intelligenceframeworks and libraries that every software engineer/ developer needs to know about. I will also share their official documentation pages and some real applications of them. This will help us to know them more than just their names. Enough with the introduction. Let me introduce you to the squad! 😄
Caffe
As coffee drinker, decided to start with Caffe. Berkeley AI’s Analysis Caffe is a deep learning framework in partnership with group members.
Through its framework, language, speed, and usability are initiated. It has a solid architecture that follows configuration-defined systems and optimizes without hard coding. This is also useful for changing between CPU and GPU.
Caffe is ideal for scientific projects and industrial implementations as it can handle over 60M photos a day with a single NVIDIA GPU.
The AI framework is responsive to C++, CUDA to command line, Python, and MATLAB interfaces. Establishing a coevolutionary neural network (CNN) to recognize the picture with Caffe is very simple.
- Official page: https://caffe.berkeleyvision.org
- Some real applications:
Torch
The torch is a scientific computation system for scientific and numerical processing. It produces algorithms with pace, versatility, and usability.
The torch appears to put GPUs first and is a Tensor Library equivalent to NumPy. This is bundled in LuaJIT and has the fundamental integration of C/CUDA.
This had increased performance and promoted deep learning analysis by having a vast number of algorithms.
Torch consumers are equipped with easy-to-use libraries, thus allowing the modular application of artificial logic distributed systems.
By a versatile N-dimensional array, this improves with procedures such as cutting and indexing. It also includes linear algebra protocols and neural networks.
- Official page: http://torch.ch
- Some real applications:
Sci-kit Learn
Scikit-learn is one of the accessible methods of Artificial Intelligence and available commercially AI framework. That’s a Python program that allows both supervised and unsupervised machine learning.
It is one of the versatile Ai creation methods supporting grouping, regression, clustering algorithms and dimensional reductions, model collection, and pre-processing.
Data scientists can easily access tools from classification and multi-label algorithms to covariance estimation that use the detailed user guide provided by sci-kit learn.
Sci-kit programming arrives with capabilities such as cross-validation, controlled, and unmonitored learning algorithms.
- Official page: https://scikit-learn.org/stable/
- Some real applications:
Google Cloud AutoML
About all the tools and libraries mentioned before, Auto ML is currently one of the best and most recent additions to the arsenal of available tools to the machine learning engineer.
As stated in the overview, efficiency is essential for machine learning responsibilities. Although the advantages obtained from them are lucrative, deciding optimum hyperparameters is not a simple feat.
It’s particularly true in black box-like neural networks, where deciding items that matter becomes more and more complicated as the complexity of the network grows. One fun fact to keep in mind: Auto ML is under Google Cloud service.
- Official page: Google Cloud AutoML
- Some real applications:
Amazon Machine Learning
Amazon Web Services (AWS) has an extensive machine learning framework used by hundreds of organizations and organizations across the world. Its software integrates with core AI systems and offers a range of ready-to-use AI applications.
- Official page: AWS Machine Leanring
- Some real applications:
Conclusion
Of course, there are many other frameworks and libraries other than the listed ones. I just shared a drop of the Artificial Intelligence and Deep Learning ocean.
Artificial Intelligence is a fascinating niche in the field of computing. Every self-respecting software developer must have a committed AI development experience. At least, some background knowledge.
Becoming a good AI professional requires a powerful intellect, skill, persistence, and work ethic. If you have them, then now is the ideal and time to get into this area.
My goal in this article was to give you some insight and inspiration about this field. Hoping that you found this reading helpful.
Applied Artificial Intelligence Projects for Beginners:
https://sonsuzdesign.blog/2020/07/25/python-for-finance-the-complete-beginners-guide/
https://sonsuzdesign.blog/2020/12/17/building-a-chatbot-in-python-beginners-guide/
Sonsuz Design 
2 responses to “5 Top Artificial Intelligence Frameworks for 2021”
Hi
It’s a nice article.
It’s very interesting to read more about how AI can be used in the industry.
You would love to see my artificial intelligence online course site as well.
Thanks for sharing.
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Whoop** interesting ☺️
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