sdk VS materials

Some issues that don't make the @protected annotation as useful as it could be are that @protected elements are not hidden the in tab completion outside their scope and protected is not being inherited (and can thus be called from outside the library on those subclasses).

If you're willing to pick up another language, I actually think Dart is the best overall programming language for young beginners. It's very streamlined and well thought-out. It doesn't have all of the complexities of languages like C# and Java, but it still has everything you'd want for application development.

Google's Flutter UI framework and Dart programming language are the best way to do desktop development right now. It supports desktop app development for Windows, MacOS, and Linux. You can also compile the same app for mobile on Android and iOS.

Flutter is a mobile app development framework based on the Dart programming language, developed by Google. One can develop Android apps, iOS apps, web apps, and desktop apps using the same code with the Flutter Framework. Flutter has a large community, which is why it is the fastest-growing app development framework ever.

Dart isn't showing up yet? I thought C# would be bigger.

Flutter arrived in 2017 as a Google project promising scalable cross-platform mobile development. It offers a batteries-included approach and comes with a comprehensive set of Material UI-themed components for building your interface. It uses declarative rendering, like React Native, but relies on the Dart programming language instead of JavaScript.

Dart is a client-optimized language for fast apps on any platform.

You are likely hitting some variation of https://github.com/dart-lang/sdk/issues/51226 or https://github.com/dart-lang/sdk/issues/50082.

Learn Python: Python is the most popular language for ML and AI projects. Start by learning the basics of Python, then move on to more advanced topics. Some great resources for learning Python include: Codecademy's Python course: https://www.codecademy.com/learn/learn-python Real Python: https://realpython.com/ Mathematics: A solid understanding of mathematics, particularly linear algebra, calculus, probability, and statistics, is essential for ML. Here are some resources to help you learn: Khan Academy courses: Linear Algebra: https://www.khanacademy.org/math/linear-algebra Calculus: https://www.khanacademy.org/math/calculus-1 Probability and Statistics: https://www.khanacademy.org/math/statistics-probability 3Blue1Brown's YouTube series on Linear Algebra: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Data processing and manipulation: Familiarize yourself with popular Python libraries for data manipulation and analysis, such as NumPy, pandas, and matplotlib: NumPy: https://numpy.org/doc/stable/user/quickstart.html pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/index.html matplotlib: https://matplotlib.org/stable/tutorials/index.html Machine learning concepts: Learn about the basic concepts of ML, including supervised learning, unsupervised learning, and reinforcement learning. Some great resources include: Coursera's Machine Learning course by Andrew Ng: https://www.coursera.org/learn/machine-learning Google's Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course Fast.ai's Practical Deep Learning for Coders course: https://course.fast.ai/ Deep learning libraries: Get familiar with popular deep learning libraries such as TensorFlow and PyTorch: TensorFlow: https://www.tensorflow.org/tutorials PyTorch: https://pytorch.org/tutorials/ Specialize and work on projects: Choose an area of interest (such as natural language processing, computer vision, or reinforcement learning), and start working on projects to apply your skills. You can find datasets and project ideas from sources like: Kaggle: https://www.kaggle.com/ Papers With Code: https://paperswithcode.com/ Stay up-to-date and join the community: Follow ML blogs, podcasts, and conferences to stay current with the latest developments. Join ML communities and forums like r/MachineLearning on Reddit, AI Stack Exchange, or specialized Discord and Slack groups.

Real Python - A great place to learn python

There's many popular Python educational blogs out there. You can check out RealPython, PythonAlgos, LearnPython, and many more.

Question - Was this theme created by/for realpython.com and they've decided to open source it?

The Real Python is one of the most valuable resources for any budding Python developer — housing thousands of professional video lessons and tutorials on every aspect of the popular programming language.

Real Python