Selenium WebDriver VS examples

Selenium is an extensively used open-source automation framework for web applications. It allows for cross-browser testing by automating browser actions, making it a staple tool for end-to-end testing in diverse web development environments.

While similar to Puppeteer, Cypress, and Selenium, there are some differences. Let’s find out what they are.

browser.get('http://selenium.dev/')

Cypress is a next-generation front-end Automation testing tool built for modern web applications. It is a JavaScript-based end-to-end testing framework and is known for faster test execution as compared with other testing tools (like Selenium or Protractor).

Removing ExpectedConditions from documentation makes sense since it was removed from the codebase around 2019 already.

https://github.com/SeleniumHQ/selenium --> trunk

You won't be able to test the javascript function itself from within python, but you can exercise the front-end code using something like cypress (https://cypress.io) or the older but still respectable selenium (https://selenium.dev).

once you solve this you may run into: https://github.com/SeleniumHQ/selenium/issues/11750, so add that workaround.

Other libraries with similar functionality are Selenium, which is very popular outside the JavaScript world, and Playwright, a younger step-brother of Puppeteer.

🔗 https://selenium.dev

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.

The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.

10. TensorFlow - Make Machine Learning Work for You 🤖

AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.

#2 TensorFlow

I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?

Machine learning is the process of using algorithms and statistical models to enable computers to learn from data. There are many tools and libraries available for machine learning, but the most popular by far is TensorFlow. TensorFlow is an open-source platform for machine learning developed by Google. It has over 176k stars on Github and is used by companies such as Airbnb and Intel.