tutorials VS Bootstrap

There are plenty of tutorials for Deep Learning available, https://pytorch.org/tutorials/. Does anyone know of a tutorial or example of Image Upscaling in a similar vain to Nvidia's DLSS?

Pytorch Documentation

PyTorch: https://pytorch.org/tutorials/

There's a TON of tutorials in the pytorch tutorials section, they're pretty solid. If you know what area you're specifically interested in, check to see if you can find some relevant tutorials to start with.

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.

The code looks almost identical to this: https://github.com/pytorch/tutorials/blob/master/beginner_source/chatbot_tutorial.py

Well the pytorch documentation is pretty good. (https://pytorch.org/tutorials/)

Pytorch tutorials https://pytorch.org/tutorials/ on their official website has all the basic commands and should be easier to pickup since you already know tensorflow/ keras.

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This article serves as your comprehensive guide to mastering the art of combining Bootstrap and React seamlessly. Dive in to uncover the tips, tricks, and best practices to elevate your UI design game effortlessly.

Bootstrap is already a popular framework among the web developers. And, these free templates makes it even more convenient to use Bootstrap in your projects.

Tailwind is great, but creating everything from scratch is annoying. A nice base of components which can be extended with tailwind would be great. There are a few tailwind frameworks like Flowbite, Daisy Ui, but I like Bulma, PicoCSS and Bootstrap.

In the JavaScript world, tread cautiously on this passionate topic. https://github.com/twbs/bootstrap/issues/3057

To effectively demonstrate Secutio's capabilities for rapid web development, we've chosen the popular Bootstrap framework as a foundation. Bootstrap provides a robust and user-friendly interface, making it an ideal choice for building the project's base.

With all this preamble out of the way, we can finally focus on the app. To make it easier to build a not-awful-looking website, I installed the dash-bootstrap-components which give us access to a variety of components from the bootstrap frontend framework. This will make styling and building the app easier.

For CSS, Bootstrap is the go-to framework for many developers. But there are other popular ones too, like Angular, React, and Vue. You don't have to learn every single framework out there—just pick the ones that are most relevant to your projects and match current industry trends and your learning preferences.

For those unfamiliar with Tailwind CSS, it is a utility-first framework with pre-defined classes for you to create custom designs. Before its creation, developers who wrote CSS were limited to two options: either writing custom CSS or using a toolkit like Bootstrap. However, both approaches came with drawbacks. Writing custom CSS was a lot of work, and using Bootstrap limited you in styling unless you added custom CSS on top.

bootstrap - toolkit for styling websites. Has lots of themes and capabilities. docs

To make the demo more interesting, we will use the Bootstrap framework and Flask as the backend.