33 Must-Know AI Tools for Machine Learning, Deep Learning, and Natural Language Processing

Here provides an overview of 33 essential AI tools for machine learning, deep learning, and natural language processing. The tools include popular libraries like TensorFlow, PyTorch, and scikit-learn, as well as specialized tools like OpenCV for computer vision and NLTK for natural language processing. Each tool is accompanied by a brief summary and URL link for further information.

1. TensorFlow – An open-source software library for machine learning and data analytics, used for building and training neural networks. URL: https://www.tensorflow.org/
2. Scikit-learn – An open-source machine learning library for Python, used for classification, regression, and clustering tasks. URL: https://scikit-learn.org/stable/
3. Keras – A high-level neural networks API, written in Python and capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. URL: https://keras.io/
4. PyTorch – An open-source machine learning framework used for building and training neural networks. URL: https://pytorch.org/
5. Hugging Face Transformers – An open-source library built on top of PyTorch for natural language processing tasks, such as language translation and question answering. URL: https://huggingface.co/transformers/
6. OpenAI GPT-3 – An AI language model capable of generating human-like text, used for natural language processing tasks. URL: https://openai.com/blog/openai-api/
7. Caffe – A deep learning framework developed by the Berkeley Vision and Learning Center, used for image classification and segmentation tasks. URL: https://caffe.berkeleyvision.org/
8. Microsoft Cognitive Toolkit (CNTK) – A deep learning framework developed by Microsoft, used for image and speech recognition tasks. URL: https://docs.microsoft.com/en-us/cognitive-toolkit/
9. MXNet – A deep learning framework developed by Apache, used for image and speech recognition tasks. URL: https://mxnet.apache.org/
10. Theano – A Python library for numerical computation, used for building and training neural networks. URL: https://github.com/Theano/Theano
11. Torch – An open-source machine learning library, used for building and training neural networks. URL: https://pytorch.org/
12. IBM Watson Studio – A cloud-based platform that provides tools for building and deploying machine learning models. URL: https://www.ibm.com/cloud/watson-studio
13. IBM Watson Assistant – A tool for building chatbots and virtual assistants using natural language processing and machine learning. URL: https://www.ibm.com/cloud/watson-assistant/
14. Dialogflow – A conversational AI platform for building chatbots and virtual assistants. URL: https://cloud.google.com/dialogflow
15. Wit.ai – A natural language processing platform for building chatbots and virtual assistants. URL: https://wit.ai/
16. Amazon SageMaker – A cloud-based machine learning platform for building, training, and deploying machine learning models. URL: https://aws.amazon.com/sagemaker/
17. Amazon Rekognition – A computer vision platform that provides image and video analysis capabilities. URL: https://aws.amazon.com/rekognition/
18. Amazon Lex – A natural language processing service for building chatbots and virtual assistants. URL: https://aws.amazon.com/lex/
19. Google Cloud AI Platform – A cloud-based platform for building and deploying machine learning models. URL: https://cloud.google.com/ai-platform
20. Google Cloud AutoML – A suite of machine learning products that enables businesses with limited ML expertise to build high-quality custom models. URL: https://cloud.google.com/automl
21. Google Cloud Speech-to-Text – A service that converts audio to text using deep learning models. URL: https://cloud.google.com/speech-to-text/
22. Google Cloud Translation – A service that provides machine translation capabilities. URL: https://cloud.google.com/translate/
23. Google Cloud Natural Language – A service that provides natural language processing capabilities. URL: https://cloud.google.com/natural-language
24. AllenNLP – An open-source natural language processing library built on PyTorch, used for language modeling and text classification tasks. URL: https://allennlp.org/
25. FastAI – An open-source library built on PyTorch that simplifies the process of building and training deep learning models. URL: https://www.fast.ai/
26. CatBoost – A gradient boosting library used for classification and regression tasks, with built-in support for categorical features. URL: https://catboost.ai/
27. XGBoost – A gradient boosting library used for classification and regression tasks. URL: https://xgboost.readthedocs.io/
28. LightGBM – A gradient boosting library used for classification and regression tasks, with high efficiency and low memory usage. URL: https://lightgbm.readthedocs.io/
29. TensorFlow.js – A library for building and training machine learning models in JavaScript, used for browser-based applications. URL: https://www.tensorflow.org/js
30. H2O.ai – An open-source machine learning platform that provides tools for building and deploying machine learning models. URL: https://www.h2o.ai/
31. DataRobot – A cloud-based platform that automates the process of building and deploying machine learning models. URL: https://www.datarobot.com/
32. Databricks – A cloud-based platform for data engineering, machine learning, and analytics, built on Apache Spark. URL: https://databricks.com/
33. Seldon – An open-source platform for deploying and managing machine learning models at scale. URL: https://www.seldon.io/

I want this list maybe help you discover more and code cool apps.