Busywork VS Keras
Compare Busywork VS Keras and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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Busywork is the no-code platform for making managed and production-ready backends & API's. If you can connect the dots, you can make anything with Busywork. Our powerful drag and drop builder lets you and your team build any serverless application with ease.
Busywork comes with managed databases, that don't distinguish between structured data, files or images. We store whatever you like and keep it safe. Combine text, strings, and media to build meaningful and rich data for your application. You will not spend a second worrying about whether to use SQL or NoSQL. You store whatever you need, we make sure it runs.
What you deploy will be running smoothly in the cloud within minutes. Prototype to production happens with the click of a button. You will be able to implement new features into your product at a faster pace. Iterate within days and not weeks. Making and deploying API's, backends and entire workflows happens in hours and not months.
BusyworkBased on our record, Keras seems to be more popular. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 month ago
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow. - Source: dev.to / about 2 months ago
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks. - Source: dev.to / 12 months ago
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them. Source: about 1 year ago
Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more. - Source: dev.to / over 1 year ago
Bubble.io - Building tech is slow and expensive. Bubble is the most powerful no-code platform for creating digital products.
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Nucode - #1 no code maker community
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
zeroqode - Build your app up to 10x faster with no-code app templates
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.