speed-comparison
Keras
Our great sponsors
speed-comparison | Keras | |
---|---|---|
9 | 78 | |
422 | 60,937 | |
- | 0.6% | |
4.6 | 9.9 | |
2 months ago | 3 days ago | |
Earthly | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
speed-comparison
- Douglas Crockford: “We should stop using JavaScript”
-
How often do you guys actually use C?
For example, Java runs on the JVM (Java Virtual Machine) instead of running directly on the hardware, and it also has a garbage collector to handle memory management. Running on a virtual machine means your code is more abstracted: you only have to worry about the JVM and not about the platform you’re running on (since the JVM is the platform), and it’s more portable since your code can go on anything that runs the JVM. But running the JVM as an intermediate layer takes more computing power and so does running garbage collection, meaning that you experience a performance penalty. Here’s one benchmark I could find comparing the use of different programming languages to compute pi, in which Java took about 3x as long as C to complete the same task
-
AITA for telling my 9 y/o daughter she sucked for not writing professional-level code?
Or you've got the speed comparisons (https://github.com/niklas-heer/speed-comparison) -- Python is probably something like 10% the speed of C/C++ (although, like I said, 99% of the time that's comparable to premature optimization).
- sou iniciante e com uma dúvida, python é realmente lento? ou é só meme?
-
Why does Julia use jit?
Looks like a PR was merged yesterday to make the code more simd friendly https://github.com/niklas-heer/speed-comparison/pull/52
- speed comparison of various programming languages, Julia (AOT) is on fire!!!
-
An Apple fan walks into a bar....
Sure, they could have chosen Python. But I doubt the language differences account for even a noticeable percentage of the slowness of Brew.
- There is framework for everything.
Keras
-
Library for Machine learning and quantum computing
Keras
-
My Favorite DevTools to Build AI/ML Applications!
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.
- Release: Keras 3.3.0
-
Getting Started with Gemma Models
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.
-
Keras 3.0
All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467
You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/
- Keras 3: A new multi-back end Keras
-
Can someone explain how keras code gets into the Tensorflow package?
I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
-
How popular are libraries in each technology
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.
-
List of AI-Models
Click to Learn more...
-
Official Question Thread! Ask /r/photography anything you want to know about photography or cameras! Don't be shy! Newbies welcome!
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.
What are some alternatives?
arl - lists of most popular repositories for most favoured programming languages (according to StackOverflow)
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
OpenCV - Open Source Computer Vision Library
scikit-learn - scikit-learn: machine learning in Python
docx4j - JAXB-based Java library for Word docx, Powerpoint pptx, and Excel xlsx files
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pivotnacci - A tool to make socks connections through HTTP agents
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Apache ZooKeeper - Apache ZooKeeper
tensorflow - An Open Source Machine Learning Framework for Everyone
NumPy - The fundamental package for scientific computing with Python.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.