Making friends on social media when the platform recommends people known to you;
Investing in cryptocurrencies, forex, or other investment plans with the help of AI-powered bots;
Watching movies that are recommended by Netflix coincidently matches your interest;
Your payment app reminds you of paying your premiums and bills;
Your phone gets unlocked by looking at you;
AI is everywhere in our lives; Isn’t it?
With so many capabilities, making a career in AI can be really amazing. If you are willing to do so, a masters degree online can serve the purpose for you.
With artificial intelligence, it has become easy for IT pros to deal with huge amounts of data. It is AI that has powered many applications across every industry. Today, AI tools and frameworks are being employed in industries to help them carry out various tedious and repetitive tasks. AI is continuously revolutionizing the way businesses function and power major innovative developments.
These AI tools help data scientists, AI Engineers, and other AI experts to deal with the data easily so that they can focus on other architectural and designing stuff. These AI tools help organizations in optimizing workflows and networks to improve the efficiency of operations significantly.
This article will let you know about various popular AI tools that are used by AI experts across the world.
Top AI Tools You Should Know
1.Scikit-Learn
One of the most popular libraries in Machine Learning, scikit-learn is known for its administered and unsupervised learning computations. The features of this library include supervised learning algorithms, data mining assignments, cross-validation, bunching, feature extraction, relapse and order, and more. This is why Scikit-learn is considered a go-to library for AI professionals.
It is built on SciPy that includes various efficient libraries of Python including Matplotlib, NumPy, Pandas, IPython, and SymPy.
2.PyTorch
When you need a quicker development of projects, PyTorch is a great choice. PyTorch has more than 22k stars. It is easily accessible on GitHub.
3.TensorFlow
This is the most sought-after deep learning library. Developed by Google, this is an open-source Python-friendly library. When you need to perform complex numerical computations or to make future predictions accurately, TensorFlow is one of the best AI development tools that can facilitate these computations.
As an AI developer, you can focus on the working of the application rather than getting engaged with algorithms. TensorFlow oversees everything that works behind the scenes. It allows you to develop neural networks and also design graphical visualizations on Tensorboard.
4.Theano
Theano is basically folded over Keras that runs in parallel with Theano. It is a moderate library of Python that allows discovery that executes on Theano or TensorFlow. Theano exploits the desktop’s GPU as well as CPU.
5.Caffe
Caffe is a deep learning framework that is specifically crafted with speed, expression, and modularity in consideration. Some of the special features of Caffe include:
●Extensible code that facilitates active development
●Expressive architecture that encourages innovation and application
●The speed that makes Caffe a perfect choice for industrial deployments and research experiments
●With huge community support, Caffe powers startup prototypes, academic research projects, and large-scale applications in speech, vision, and multimedia
6.Keras
Keras is an ideal choice if you like working with Python. It is a high-level Python library meant for neural networks with Theano or TensorFlow at the back-end. It employs best practices for minimizing cognitive load.
Keras reduces the number of user steps needed for common use cases; offers simple and consistent APIs, and provides actionable and clear error messages. Keras also has developer guides and extensive documentation.
Keras makes it easy to make new experiments or try new ideas quicker than ever.
7.CNTK
CNTK or the Microsoft Cognitive Toolkit is a popular AI tool that describes neural networks as a series of calculative steps through a directed graph. This deep learning framework allows you to merge popular models such as CNNs (Convolutional Neural Networks), feed-forward DNNs(Dense Neural Networks), and RNNs (Recurrent Neural Networks). It can also implement SGD (Stochastic Gradient Descent) and error propagation to learn with automatic differentiation as well as parallelization across different GPUs and servers. You can try CNTK under an open-source license.
8.Google ML Kit
Machine Learning beta SDK by Google is specifically meant for mobile developers that enables you to build robust and personalized features on iOS and Android phones.
With this Machine Learning Kit, you can embed ML technologies with application-based APIs that may run on the cloud or on the device. This may include common features such as text and face recognition, image labeling, barcode scanning, and more.
If your existing API doesn’t suit your use case, the Google ML kit allows you to develop your own TensorFlow Lite models.
9.Auto ML
Auto ML is one of the strongest AI tools available in the tools’ arsenal. Optimization is the most important task in machine learning because optimal parameters provide amazing benefits. But optimization of parameters is not an easy task, especially when the depth of neural networks increases. It is here that AutoML enables you to optimize models quickly and easily where software helps in building software.
AutoML is an ideal library for beginners as well.
10.OpenNN
From complete beginners to experienced professionals, OpenNN is ideal for everyone working on AI models. It provides versatile features for advanced analytics.
Neural Designer, a tool offered by Open NN, is designed for advanced analytics that provides you with tables and graphs to interpret data entries effectively.
Conclusion
Apart from the popular AI tools listed above, there are many more tools that enable you to build robust AI models. In order to go hands-on with the most widely used AI tools, an online training course can be a one-stop solution.
Some of the popular training courses, such as that of Simplilearn, enable you to get hands-on practice with these tools by making you go through real-life projects so that you can start building your AI-based applications soon.
Enroll Yourself Now!