Introduction

Artificial intelligence (AI) and Machine learning (ML) are everywhere and in every industry. It allows processing huge amounts of data letting experts put the analysis to better use. With the growth in AI and ML, several frameworks and Artificial Intelligence tools have been made available to developers and scientists. Listed below are the popular artificial intelligence tools and frameworks that are available in the market.

List of Artificial Intelligence Tools

Below is the Artificial Intelligence tools list of the most popular frameworks and tools that are available in the market. You can choose from these artificial intelligence tools depending on what best meets your requirements

  1. Scikit Learn
  2. Tensorflow
  3. Theano
  4. Caffe
  5. MxNet
  6. Keras
  7. PyTorch
  8. CNTK

1. Scikit Learn

One of the popular tools used in the ML libraries, Scikit learn, underpins the unsupervised and administered calculations. The precedent can incorporate the calculated and direct relapses, bunching, choice trees, etc. The tool expands to SciPy, Python, and the NumPy libraries. There are plenty of calculations for data mining and regular AI assignments. Even the undertakings like feature determination, changing information, and ensemble techniques take just a few lines to execute. If you wish to use a tool for fledgling, then Scikit learn is the perfect instrument that you can work along with.

2. Tensorflow

Calculations could be profound, and you may attempt them but are these always essential? Well, not always. However, if done right, are these calculations worthy? The answer to this is an absolute YES. Tensorflow lets you compose a Python program, and then you can run it and arrange it on the GPU or the CPU. So if you want to run the program on the GPUs, then you do not have to compose it at the CDA or the C level.

Tensor makes use of many-layered hubs that allow rapid setting up, training, and sending counterfeit neural systems along with the huge datasets. This is what lets Google recognize those questions that come in a photographic form. It also lets Google comprehend the words that are expressed verbally in the application for voice acknowledgment.

3. Theano

The Theano is folded over the Keras. Keras is a Python library moderately that allows profound discovery that runs on the Tensorflow or the Theano. Theano was made to create models of profound learning and make them simple and quick to be feasible to put into some innovative work. It runs on Python and can be executed on GPUs and CPUs. Theano can exploit the GPU of the PC. This lets it make escalated information counts, which are many times more than when it is kept to run on the CPU only. The speed of Theano makes it highly profitable to carry out any complex computations.

4. Caffe

Caffe is a structure that offers profound learning and is made with speed, articulation, and quality, which is its topmost priority. This has been created by Berkeley Vision and Learning Center (BVLC). It is a C library along with an interface of Python.

5. MxNet

MxNet allows using a forgetful back prop to trade the computations time in return for memory. This is particularly useful in the case of a recurrent net that is in a long sequence. The tool has been built to ensure scalability, and it is easy to support the multi-machine and multi-GPU training. It is equipped with features like writing the custom layers in a high-level language. The framework is not governed by any corporation, which makes it healthy as an open source as it is a framework that has been developed by a community.

6. Keras

If you like python and the way it does things, then Keras is just what you need. This is a high-end library that takes care of neural networks, which it does, making use of Theano and Tensorflow that is used in the backend. It picks up the architecture that applies to particular problems. It assists in recognizing problems through images which it does use weights. It configures a network for result optimization. Keras offers a very abstract structure that can be converted to any other framework for performance or compatibility.

7. PyTorch

An artificial system that has been created by Facebook, PyTorch code is easily accessible on Github. It has more than 22000 stars. The framework has been highly in demand in the last few years and is in a continuous development phase.

8. CNTK

CNTK lets the users release and then combine the popular types of models, including DNNs, CNNs, and RNNs or LSTMs. It makes use of the SGD or the stochastic gradient descent, which learns with parallelization and automatic differentiation that runs across many servers and GPUs. CNTK can be tried out by all because it offers open-source licenses.

Conclusion

The above is the list of the top Artificial Intelligence tools and Frameworks that are used by engineers and data scientists to solve some real-life problems, which in turn makes the architecture underlying the system to get more robust and better.

There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this Machine Learning And AI Courses by Jigsaw Academy.

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