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20 AI Tools You should have in your tool Box from Beginner to Advanced

Looking to start exploring AI tools to improve your Productivity and Efficiency?

Here are 20 AI tools you might find handy, complete with what they do, when to use them, and where to find them:

1. ChatGPT (https://chat.openai.com/chat) – A large language model that can engage in natural conversations, answer questions, and assist with a variety of tasks such as writing, analysis, and problem-solving.

2. Dall-E 2 (https://www.openai.com/dall-e-2/) – An AI system that can generate, edit, and create images from textual descriptions.

3. Midjourney (https://www.midjourney.com/) – An AI-powered image generation tool that can create unique and imaginative visuals based on text prompts.

4. Stable Diffusion (https://stability.ai/blog/stable-diffusion-public-release) – An open-source AI model that can generate high-quality images from text descriptions.

5. Whisper (https://openai.com/blog/whisper/) – An AI model that can transcribe and translate audio files with high accuracy.

6. Replika (https://replika.com/) – An AI chatbot that can engage in personalized conversations and provide emotional support.

7. Anthropic’s Claude (https://www.anthropic.com/language-model) – An AI language model that can assist with a wide range of tasks, from writing to analysis and problem-solving.

8. Hugging Face Transformers (https://huggingface.co/transformers) – A collection of pre-trained models for natural language processing tasks, including text classification, question answering, and text generation.

9. Gradio (https://www.gradio.app/) – A Python library that allows you to quickly create customizable web interfaces for your machine learning models.

10. Streamlit (https://streamlit.io/) – A Python library that makes it easy to build and deploy web applications for your machine learning models.

11. TensorFlow (https://www.tensorflow.org/) – An open-source machine learning framework for building and deploying AI models.

12. PyTorch (https://pytorch.org/) – An open-source machine learning library for Python, known for its flexibility and ease of use.

13. Scikit-learn (https://scikit-learn.org/stable/) – A machine learning library for Python that provides simple and efficient tools for data mining and data analysis.

14. Keras (https://keras.io/) – A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

15. Anaconda (https://www.anaconda.com/) – A free and open-source distribution of the Python and R programming languages for data science and machine learning.

16. Jupyter Notebook (https://jupyter.org/) – An open-source web application that allows you to create and share documents that contain live code, visualizations, and narrative text.

17. Google Colab (https://colab.research.google.com/) – A cloud-based Jupyter notebook environment that allows you to write and execute code without the need for local setup.

18. Weights & Biases (https://wandb.ai/) – A platform for tracking and visualizing machine learning experiments, with features for collaboration and model versioning.

19. Hugging Face Spaces (https://huggingface.co/spaces) – A platform for deploying and sharing your machine learning models as web applications.

20. Amazon SageMaker (https://aws.amazon.com/sagemaker/) – A fully managed machine learning service from Amazon Web Services that allows you to build, train, and deploy your models at scale.

Dr. Seun Ogunmola

https://linkedin.com/in/seunogunmola

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