Write Thesis in two weeks; Top 36 Useful Artificial Intelligence (AI) Apps you should Know

Top 36 Useful Artificial Intelligence (AI) Apps you should Know

 Dr. Muhammad Uziar Ul Hassan     Sahib Khan Ph.D. Edu Scholar 

Introduction

Writing a thesis is a difficult task that takes a lot of time and energy. But with the introduction of AI tools, thesis writing has become much simpler and more effective. With the aid of AI tools, a well-researched and polished thesis may be created in just two weeks. Thesis writing requires a variety of skills, including topic selection, research, planning, and writing. AI tools can help. These technologies analyze massive volumes of data using machine learning and natural language processing algorithms to produce insights and suggestions that help improve writing. The structure and flow of the thesis can also be improved, and AI tools can also suggest ways to employ language more skillfully. Researchers may focus on other crucial areas of their work, such as data processing and interpretation, by using AI technologies to save time and energy. We produce a list of AI tools for you to explore and get help in writing your thesis within two weeks. 

1. TensorFlow:  TensorFlow is an open-source machine learning library developed by Google. It provides a range of tools and resources for building and training machine learning models, including support for deep learning, neural networks, and natural language processing.

2.  PyTorch: PyTorch is another popular open-source machine learning library that provides tools for building and training neural networks. It is known for its ease of use and flexibility, and it has become a popular choice for researchers and developers alike.

3. Keras: Keras is a high-level neural networks API written in Python that runs on top of TensorFlow. It provides a simple and easy-to-use interface for building and training neural networks, making it a popular choice for beginners and experts alike.

4.     Scikit-learn: Scikit-learn is a popular machine-learning library in Python that provides tools for data preprocessing, feature extraction, and model selection. It includes a range of algorithms for both supervised and unsupervised learning.

5.     OpenCV: OpenCV is an open-source computer vision library that provides tools for image and video processing, object detection, and face recognition. It is widely used in industry and academia for a range of computer vision applications.

6.     NLTK: NLTK (Natural Language Toolkit) is a Python library that provides tools for natural language processing (NLP). It includes a range of algorithms for tokenization, stemming, and part-of-speech tagging, making it a popular choice for building NLP applications.

7.    IBM Watson: IBM Watson is a suite of AI tools that includes natural language processing, speech-to-text, and image recognition. It is widely used in industry for a range of applications, including customer service, healthcare, and finance.

8.     Microsoft Cognitive Services: Microsoft Cognitive Services is a collection of AI tools that provides services for computer vision, speech recognition, and natural language processing. It is used by developers and businesses to build intelligent applications.

9.     Google Cloud AI Platform: Google Cloud AI Platform provides a range of AI tools and services, including machine learning APIs, TensorFlow support, and data processing tools. It is widely used in the industry for a range of applications, including fraud detection, recommendation systems, and image recognition.

10. Amazon SageMaker: Amazon SageMaker is a fully managed machine learning platform that provides tools for building, training, and deploying machine learning models. It includes support for popular machine learning frameworks such as TensorFlow and PyTorch, making it a popular choice for developers and researchers

11. Hugging Face: Hugging Face is an open-source library that provides tools for natural language processing, including pre-trained models, transformers, and pipelines. It is used by developers and researchers to build and deploy NLP models quickly and easily.

12. RapidMiner: RapidMiner is an open-source platform that provides tools for data science and machine learning. It includes a range of algorithms and data visualization tools, making it a popular choice for building predictive models and analyzing data.

13. DataRobot: DataRobot is an automated machine learning platform that provides tools for building, training, and deploying machine learning models. It is used by businesses and organizations to automate and scale their machine-learning efforts.

14. BigML: BigML is a cloud-based platform that provides tools for machine learning and predictive analytics. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models and analyzing data.

15. IBM SPSS: IBM SPSS is a statistical software package that provides tools for data analysis, predictive modeling, and decision-making. It is used by businesses and organizations to analyze data and build predictive models.

16. Weka: Weka is an open-source machine learning library that provides tools for data preprocessing, feature selection, and model selection. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models.

17. MATLAB: MATLAB is a programming language and development environment that is widely used in scientific computing and engineering. It includes a range of tools and functions for machine learning, including support for deep learning and neural networks.

18. Caffe: Caffe is a deep learning framework that is widely used for image and video recognition tasks. It provides a range of pre-trained models and tools for building custom models, making it a popular choice for computer vision applications.

19. PyBrain: PyBrain is an open-source machine learning library that provides tools for neural networks and reinforcement learning. It includes a range of algorithms for both supervised and unsupervised learning, making it a popular choice for building predictive models.

20. R: R is a programming language and environment for statistical computing and graphics. It includes a range of tools and packages for machine learning and data analysis, making it a popular choice for researchers and data scientists

21. Microsoft Azure: Microsoft Azure is a cloud computing platform that provides a range of AI services, including machine learning, cognitive services, and bot services. It is widely used by businesses and organizations to build intelligent applications.

22. Google Dialogflow: Google Dialogflow is a natural language processing platform that provides tools for building conversational agents, chatbots, and voice interfaces. It is widely used by businesses and organizations to automate customer service and support.

23. Clarifai: Clarifai is a computer vision platform that provides tools for image and video recognition, tagging, and analysis. It is used by businesses and organizations to automate visual content analysis and tagging.

24. Wit.ai: Wit.ai is a natural language processing platform that provides tools for building conversational agents, chatbots, and voice interfaces. It is widely used by developers and businesses to build intelligent applications.

25. IBM Watson Studio: IBM Watson Studio is a cloud-based platform that provides tools for data science, machine learning, and deep learning. It includes a range of algorithms and models, making it a popular choice for building predictive models and analyzing data.

26. Apache Mahout: Apache Mahout is an open-source machine learning library that provides tools for clustering, classification, and recommendation engines. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models.

27. Orange: Orange is an open-source data visualization and machine learning library that provides tools for data mining, data visualization, and predictive modeling. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models.

28. Azure Machine Learning Studio: Azure Machine Learning Studio is a cloud-based platform that provides tools for building, training, and deploying machine learning models. It includes support for popular machine learning frameworks such as TensorFlow and PyTorch, making it a popular choice for developers and researchers.

30. CNTK: CNTK is an open-source deep learning library that provides tools for building and training deep learning models. It includes support for convolutional neural networks and recurrent neural networks, making it a popular choice for computer vision and natural language processing tasks.

31. Apache Spark MLlib: Apache Spark MLlib is a machine learning library that provides tools for classification, regression, clustering, and collaborative filtering. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models.

32. PySpark: PySpark is the Python API for Apache Spark, a distributed computing platform for big data processing. It provides tools for machine learning and data analysis, making it a popular choice for big data projects. 

33. Fast.ai: Fast.ai is an open-source deep learning library that provides tools for building and training deep learning models. It includes support for computer vision, natural language processing, and tabular data, making it a popular choice for building predictive models.

34. PyCaret: PyCaret is an open-source machine learning library that provides tools for building, training, and deploying machine learning models. It includes a range of algorithms for supervised and unsupervised learning, making it a popular choice for building predictive models.

35. Optuna: Optuna is an open-source hyperparameter optimization library that provides tools for optimizing machine learning models. It includes a range of algorithms for tuning hyperparameters, making it a popular choice for improving the performance of predictive models.

36. Databricks: Databricks is a cloud-based platform for big data processing and machine learning. It provides tools for building and training machine learning models at scale, making it a popular choice for big data projects.

 Conclusion

For researchers and students who wish to write a thesis quickly and effectively, AI technologies have emerged as a helpful resource. These technologies have the capacity to analyze enormous amounts of data, offering insightful analyses and recommendations that might enhance a thesis' organization and coherence. Additionally, they can support proficient language use and natural language processing. Popular libraries and platforms like TensorFlow, PyTorch, and Scikit-learn are included in this list of the top 36 AI tools, along with well-known platforms like IBM Watson, Google Cloud AI, and Microsoft Cognitive Services. With the aid of these technologies, researchers and students can concentrate on other crucial facets of their job, such as the processing and analysis of data. AI tools offer an excellent opportunity to improve the quality of a thesis while saving time and energy. AI techniques provide a fantastic opportunity to enhance thesis quality while saving time and effort