Have you explored these free AI Technical Tools?

Have you explored free tools available for various aspects of AI, from machine learning and deep learning frameworks to natural language processing libraries and data visualization tools. Here are some of the most popular ones:

Machine Learning and Deep Learning Frameworks

1. TensorFlow: An open-source library for numerical computation and large-scale machine learning.
2. PyTorch: An open-source deep learning platform that provides a seamless path from research prototyping to production deployment.
3. Keras: An open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for TensorFlow.
4. Scikit-learn: A free software machine learning library for the Python programming language.

Natural Language Processing (NLP)

1. NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing for English.
2. SpaCy: An open-source software library for advanced NLP in Python.
3. Hugging Face Transformers: A library providing general-purpose architectures for NLP, with pre-trained models.

Data Visualization

1. Matplotlib: A plotting library for the Python programming language and its numerical mathematics extension NumPy.
2. Seaborn: A Python visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.
3. Plotly: An open-source graphing library that makes interactive, publication-quality graphs online.

Data Handling and Manipulation

1. Pandas: An open-source data analysis and manipulation tool built on top of the Python programming language.
2. NumPy: A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Computer Vision

1. OpenCV: An open-source computer vision and machine learning software library.
2. DLib: A toolkit for making real-world machine learning and data analysis applications.

Model Deployment

1. TensorFlow Serving: A flexible, high-performance serving system for machine learning models, designed for production environments.
2. Streamlit: An open-source app framework for Machine Learning and Data Science teams to create beautiful, performant apps.

Integrated Development Environments (IDEs)

1. Jupyter Notebook: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
2. Google Colab: A free Jupyter notebook environment that runs entirely in the cloud.

These tools can be used individually or in combination to build and deploy AI models effectively.

Please share your experience if you already using these tools ! #AI