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AI Tools: From Software Libraries to Platforms

To help beginners navigate this rapidly evolving landscape, here is a list of essential tools associated with AI. These tools, ranging from software libraries to comprehensive platforms, are designed to facilitate various aspects of AI development, from data analysis to building complex machine learning models. Whether you're just starting out or looking to deepen your understanding of AI, these tools will provide a solid foundation for your learning and exploration.

Beginner Platforms​

These chatbots and platforms are excellent places to start exploring the possibilities of AI. The resources are a mix of paid and free.

  1. ChatGPT: ChatGPT has been pivotal in the AI chatbot revolution, offering versatile conversation-based tools.

  2. Jasper: Best suited for businesses and marketers, Jasper is a top choice as an AI chatbot.

  3. YouChat: This is an excellent ChatGPT alternative, especially for those looking for a different flavor in AI chatbots.

  4. Chatsonic by Writesonic: Ideal for news content creators, Chatsonic offers specialized features for this niche.

  5. IBM Watson Assistant: Known for personalized customer support, this chatbot uses data from customer interactions to offer tailored solutions.

  6. Ada: Utilizing machine learning, Ada improves its responses over time, providing increasingly precise solutions for customers.

  7. REVE Chat: A leading no-code chatbot platform, REVE Chat includes a visual flow builder for hassle-free chatbot creation, ideal for those without coding experience.

  8. Tidio: Highly rated for small and medium-sized businesses, Tidio offers an easy and quick chatbot editor, integrating live chat with chatbots.

  9. Gemini: A model designed from the beginning for multimodal communication, smoothly integrating text, images, video, audio, and code.

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A multimodal model is a type of artificial intelligence that can understand and process different types of information, such as text, images, videos, and audio, all at once. Imagine a robot that can read a book, look at pictures, watch a movie, listen to music, and even understand computer code. This robot doesn't just understand each of these things separately; it can make connections between them. For example, it might read a story, recognize the characters in a picture, understand the plot in a movie, hear the emotions in a song, and even create its own stories or pictures by mixing all these skills together. This is what a multimodal model does – it combines different ways of understanding the world to be more versatile and smart, just like a multi-talented person who is good at many different things.

Beginner Resources​

These resources are good starting points for those interested going deeper into the nuts and bolts of AI.

  1. DataCamp's Guide on AI: DataCamp offers a comprehensive guide for learning AI from scratch in 2023, featuring expert tips, helpful resources, and industry insights. This can be a great starting point for absolute beginners.

  2. Artificial Intelligence Certification by Stanford University: This beginners' course is offered by Stanford University, targeting candidates with a background in Computer Science and professional software engineers who are interested in AI. The course provides certification in AI.

  3. Free AI Training Courses (Tech.co): Tech.co lists the best free AI training courses available in 2023. These include offerings from renowned companies like Google and Microsoft.

  4. Coursera's AI Courses: Coursera has curated a selection of beginner-friendly AI courses that introduce learners to the fundamentals of AI.

  5. Google's AI Resources: Google offers over 20 resources including guides, podcasts, videos, courses, and interactive content. These resources cover various topics, including machine learning and TensorFlow APIs. Many of these resources are free.

  6. DeepLearning.AI Courses: DeepLearning.AI courses range from beginner (Generative AI for Everyone) to more advanced (Machine Learning Engineering for Production). Certification is available with many of these.

  7. Resources to Learn ChatGPT and the OpenAI API by u/richie_cotton: A large list of resources of online courses, tutorials, and repos from Redittors.

Additional Software and Platforms​

This is a list of more advanced resources that are compliments to AI. The list includes programming languages, libraries, APIs, frameworks, and more.

  1. TensorFlow: An open-source library developed by the Google Brain team, widely used for machine learning and deep learning applications.

  2. PyTorch: Developed by Facebook's AI Research lab, PyTorch is a popular open-source machine learning library, known for its flexibility and ease of use in research settings.

  3. Keras: A high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano. It is known for its user-friendliness and modularity.

  4. Scikit-learn: A free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms.

  5. Jupyter Notebook: An open-source web application that allows the creation and sharing of documents containing live code, equations, visualizations, and narrative text.

  6. Pandas: A software library written for data manipulation and analysis in Python. Particularly well-suited for handling structured data.

  7. 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.

  8. MATLAB: A high-level language and interactive environment for numerical computation, visualization, and programming.

  9. Microsoft Azure AI: A set of cloud services that offer machine learning, cognitive services, conversational AI, and more.

  10. Amazon Web Services (AWS) AI: Provides a variety of AI services and tools for machine learning, text analysis, image and video analysis, etc.

  11. Google Cloud AI: Offers AI and machine learning services on Google Cloud Platform, with tools like AI Platform, Vision AI, and more.

  12. OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms.

  13. NVIDIA CUDA: A parallel computing platform and application programming interface model created by NVIDIA.

  14. IBM Watson: An AI platform for businesses, integrating AI into various applications like conversation, language, speech, vision, and data analysis.

  15. Apache Spark: An open-source, distributed processing system used for big data workloads.

  16. H2O.ai: An open-source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform.

  17. ELKI: Environment for Developing KDD-Applications Supported by Index-Structures is a software framework for developing data mining algorithms in Java.

  18. RapidMiner: A data science platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

  19. Prolog: A logic programming language associated with artificial intelligence and computational linguistics.

  20. AutoML tools: Automated machine learning tools like Google's AutoML, AutoKeras, and others, which automate the process of selecting and tuning machine learning models.

  21. Hugging Face Spaces: A collection of Machine Learning based apps made by the Hugging Face community.