awesome-teachable-machine VS examples

References: Scratch Blockly Google Teachable Machine LEGO Spike Prime

Google’s Teachable Machine: Create ML models without coding.

Not sure if I've seen anything of the sort, seems rather specific. Maybe try a Teachable Machine project? https://teachablemachine.withgoogle.com/

Train a computer to recognize your images, sounds, and poses. Use this resource to gain a better understanding.

[Teachable Machine](https://teachablemachine.withgoogle.com/)

We will create an machine learning model that can classify Arabic and English books. To collect, train, and test data, we will use Teachable Machine from Google.

a lot of places! but for a high schooler, better to focus at what you want to do first. or if you still haven't gotten any idea, try a simple explanation on what ml is without the math on youtube and tinker around a no code machine learning platform like https://teachablemachine.withgoogle.com/

As we prepare for the next phase in AI evolution, embracing decentralized approaches and synthetic data generation will be essential. Developers are encouraged to explore technologies like TensorFlow, Prophet, and platforms hosted on Ocean Protocol and License Token for further exploration. Additionally, more detailed discussions on these topics can be found in in-depth Dev.to posts such as Apache Mahout: A Deep Dive into Open Source Innovation and Funding Models.

Python's status as the preferred language for artificial intelligence has been solidified by its ease of use, large library (such as TensorFlow, PyTorch, and scikit-learn), and active community.

TensorFlow.js: An open-source library that allows you to run machine learning models directly in the browser.​

Installation of PyTorch or TensorFlow

Emerging trends like decentralized data markets, synthetic time series generation, and enhanced NFT-based monetization models underline the vibrant future awaiting AI-driven predictive analytics. For developers and industry leaders, familiarizing yourself with tools like TensorFlow, Prophet, and Nixtla’s TimeGPT is crucial to stay ahead in this dynamic field.

Local Machine Learning Systems: Self-hosted solutions using open-source tools like TensorFlow or vendor solutions like Traceable AI.

While Deutsche Telekom’s journey with open source is filled with promise, it does not come without challenges. Issues like maintaining high code quality, navigating intellectual property rights, and adapting to rapidly changing technological landscapes are constant hurdles. However, these challenges are also the source of immense opportunity. For instance, by integrating cutting-edge technologies like TensorFlow and exploring future trends such as the intersection of blockchain and open source (the future of open source with blockchain integration), Deutsche Telekom is positioning itself at the forefront of innovation.

Throughout Trump's presidency, open source frameworks like Kubernetes, TensorFlow, and Hyperledger played pivotal roles in driving technology forward. These platforms were the backbone of critical technological innovations. Kubernetes streamlined container orchestration, TensorFlow democratized machine learning, and Hyperledger pushed blockchain solutions into mainstream business applications. Tech giants and startups alike harnessed these tools to create scalable, resilient infrastructures that changed how the industry approached innovation. This era also witnessed the rise of initiatives like Open Source Sponsorship, which provided much-needed financial support to evolving OSS projects. By facilitating community engagement and ensuring continuous development, sponsorship programs contributed significantly to the sustainability of open source projects.

If you're interested in diving deeper, check out the official TensorFlow documentation and experiment with different datasets and architectures. Happy coding!

This popular AI platform comes with tools for ML model creation. It’s ideal for deep learning tasks and scalable production systems. TensorFlow particularly excels in building complex models like convolutional neural networks or recurrent neural networks.