Artificial intelligence is one of the most widely used and important fields of computer science, and as we have mentioned in other articles of the special case, they help companies and professionals in various fields to perform assigned tasks. When it comes to building intelligent models, there are various tools and libraries available to users. However, some of them provide more efficient capabilities and tools to professionals. In this article, we will learn about 14 powerful libraries and frameworks that allow you to design intelligent models in a more precise way.
1. TensorFlow
TensorFlow is a powerful open source framework for building machine learning models and neural networks developed by Google. TensorFlow uses the Python programming language to define and build machine learning models. This framework provides many possibilities for simulating neural networks, deep learning, image processing, natural language processing and other artificial intelligence applications.
TensorFlow includes a collection of open source libraries and tools used to build machine learning models and neural networks. This framework simplifies the process of prediction and pattern recognition by aggregating data, building machine learning models and training them. In addition, TensorFlow uses pre-trained models to speed up the learning process. TensorFlow is very popular in implementing artificial intelligence and machine learning projects due to its high capabilities and support for various platforms. Also, it supports various libraries for image processing, natural language processing, music processing and other artificial intelligence applications.
2. PyTorch
PyTorch is an open source framework for building neural networks and deep learning models, implemented in Python. This framework was developed by the Meta developer team and has been publicly available to developers since 2016. So that it has become one of the most powerful frameworks in the field of building neural networks. PieTorch is very popular for research and implementation of various machine learning models and neural networks due to its simple and understandable design. This framework uses the Dynamic Computational Graph structure to create machine learning models, which allows users to easily modify their models according to needs. In addition, PieTorch has many features for image processing, natural language processing and other artificial intelligence applications. PiTorch supports various libraries for implementing complex neural networks such as Generative Adversarial Networks, Recurrent Neural Networks and Graph Attention Networks. Due to the fact that PiTorch uses a dynamic computing graph architecture, it is suitable for implementing complex algorithms and conducting research projects. Also, it provides facilities in the field of big data processing, optimization of models and implementation of multipurpose models to developers.
3. Keras
Cross is a framework for building neural networks and deep learning models that can be used in the Python programming language. This framework is very popular due to its simplicity and ease of use in building machine learning models and neural networks. Experts can use Cross independently, but it is mostly used as a platform for implementing neural networks in TensorFlow. Cross is a simple and understandable framework that allows users to implement their models more quickly and easily according to their needs. This framework has pre-trained models from popular neural networks such as VGG16 and ResNet50 that can be used to implement machine learning models. In addition, CROSS has many possibilities for image processing, natural language processing and other artificial intelligence applications. This framework supports various libraries for implementing machine learning models such as Recurrent Neural Networks, Attention Networks and Generative Adversarial Networks. Cross is suitable for users who are just entering the world of machine learning and neural networks due to its simplicity and ease of use. Also, this framework provides facilities for implementing distributed models and distributed processing.
4. Scikit-learn
Skate-Learn is an open source library for machine learning. This library is based on Python language and is used for small and medium projects. Typically, professionals use Skate-Learn in the field of machine learning and data mining in Python.
This library includes a set of machine learning algorithms such as regression, classification, clustering and other algorithms related to data mining and uses advanced statistical and computational algorithms to process big data. Also, it has many facilities for dividing data into training and testing sections, validation, feature selection and data preprocessing. Using its diverse and dynamic tools, Skate-Learn allows users to implement their machine learning models quickly and with high accuracy.
5. Theano
Theno is a programming frameworkIt is designed for Python language to build neural networks and deep learning models. This framework was developed by the Deep Learning Research Group of the University of Montreal and has been widely used in industry and scientific research since 2007. Theno has the ability to perform parallel computations for big data processing and provides users with a wide range of deep learning algorithms. This framework uses the Dynamic Computational Graph structure to build machine learning models, which allows users to easily modify their models according to needs. In addition, Theno provides facilities for image processing, natural language processing and other applications related to artificial intelligence, so that they do not have any problems with the implementation of neural networks.
Due to its speed and high scalability, Theno is recommended for implementing complex algorithms and large projects. Also, this framework provides benchmark capabilities for model optimization and multi-objective model implementation. However, it is not recommended for new users in the field of machine learning and neural networks due to its complexity and not so easy to use. This issue has led some experts to use frameworks such as TensorFlow and PieTorch to implement machine learning models and neural networks.
6. Coffee
Kafi is a powerful and high-level framework for building and training complex neural networks. This framework is written in C Plus Plus language and was first used in the field of machine vision and object recognition, but today it is used in many fields of artificial intelligence such as natural language processing, machine translation, speech recognition and similar cases. One of the important reasons that made us add Kaffee to our list is the many possibilities it provides for image processing and natural language processing. So that compared to some frameworks, it provides the most complete tools for building neural networks to experts.
This framework is mainly used to implement complex algorithms due to its high speed and scalability. Typically, professionals use Kafi because of the ready-made models it provides. The above approach allows users to use ready-made models such as VGG and ResNet and train them with their own data. In general, Kafi is a very powerful framework for designing and training complex neural networks, which is used to implement machine learning models and neural networks in industry and scientific research due to its scalability and support for ready-made models.
7. MXNet
A text game framework for building machine learning models and neural networks. This framework supports various programming languages such as Python, Java, C++, etc. and has the capability of distributed processing. This framework provides a wide range of technical features for implementing complex neural networks, some of which are as follows:
Support for parallel computing: MXNET uses parallel computing to process big data and accelerate the training process of neural networks. This framework has the ability to use different computing systems to train models faster.
Support for multiple programming languages: MXNET supports multiple programming languages such as Python, Java, C++, and Ruby, allowing users to interact with MXNET in a language they are familiar with.
Support for different hardware: MXNET has the ability to support different hardware such as central processors, graphics and programmable integrated circuits (FPGA).
Support for different platforms: MXNET supports different operating systems such as Linux, Windows and Macintosh. Therefore, users can choose the target platform according to their needs.
Support for Complex Neural Networks: MXNET supports various types of complex neural networks such as recurrent neural networks, convolutional networks, generative adversarial networks, and attentional networks.
Reinforcement Learning Support: MXNET allows professionals to implement reinforcement learning algorithms using this framework.
Support for natural language processing: MXNET supports natural language processing models such as machine translation models and text generation models.
Support for Recurrent Long-Term Memory (LSTM) networks: MXNET supports Recurrent Long-Short-Term Memory (LSTM) networks, allowing users to train their models to solve complex problems such as speech recognition and machine translation.
Support for semi-supervised learning: MXNET supports semi-supervised learning methods such as transfer learning, which allows users to train better models using less data.
8. Torch
Another open source framework for building machine learning models and neural networks. This framework supports Python and Lua language and is a suitable option for researchers active in the field of artificial intelligence and machine learning.
9. Hugging Face
is an open source library for natural language processing. This library supports Python language and is a suitable option for building NLP projects, information retrieval and text analysis.
10. OpenCV (OpenCV)
is an open source library for image processing. OpenCV includes tools for image and video processing, geometric transformation, object detection, face detection, motion detection, feature extraction, color detection, and multidimensional image and video review. This library is used in various fields such as machine vision, robotics, medicine, industry, sports, video games, etc.
11. NLTK
NLTK is the most powerful text game library available in the field of natural language processing. Python programmers can use this library to build NLP projects, text processing, text analysis, etc. For natural language processing, NLTK uses various algorithms such as grammar analysis, neural networks, classification, etc. Using this library, you can process texts and convert words into feature vectors. Also, you can use this library for text preprocessing, grammar analysis, semantic analysis, sentiment analysis and many other natural language processing applications. NLTK includes tools for processing text in various forms such as words, sentences, and documents.
12. Gensim
Gensim is another open source library in the field of text mining and natural language processing in the Python programming language. This library is used to process text and text-based data such as scientific articles, news, social texts, etc. One of the main uses of this library is the implementation of Topic Modeling algorithms. Using this library, you can convert documents into Word Vectors and use these vectors to find different topics in texts. Gensim uses various techniques to model the subject, including Latent Dirichlet Allocation and Latent Semantic Analysis. Also, this library provides text processing tools such as preprocessing, vectorization, and sentiment analysis. Other features of Gensim include support for Word2Vec, FastText and Doc2Vec models, which are used for vectorization of words and documents.
13. Pandas
is an open source library for structured data processing. This library, which can be used in Python, is used for data mining projects, data analysis, etc.
14. Matplotlib
Matplotlib is a graphical library that can be used in the Python programming language, which is used to create graphs and display data graphically. This library is very powerful and extensive and offers many possibilities for creating charts and images. Matplotlib uses a series of tools to convert various data into various graphs and allows users to create various graphs from their data. This library supports line charts, bar charts, dot charts, scatter charts and 3D charts. In addition, professionals can save charts in various formats such as PNG, PDF, SVG. This library is used in many applied fields such as data science, engineering, physics, statistics, etc.
