MLP Classifier
Keras
| MLP Classifier | Keras | |
|---|---|---|
| - | 82 | |
| 226 | 61,855 | |
| - | 0.5% | |
| 0.0 | 9.9 | |
| over 7 years ago | 4 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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.
MLP Classifier
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Tracking mentions began in Dec 2020.
Keras
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Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.
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Las 10 Mejores Herramientas de Inteligencia Artificial de Código Abierto
(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/92cup4lywcjfq83xg0ea.png)
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Using Google Magika to build an AI-powered file type detector
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.
- Side Quest #3: maybe the real Deepfakes were the friends we made along the way
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Library for Machine learning and quantum computing
Keras
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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
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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.
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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
What are some alternatives?
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
scikit-learn - scikit-learn: machine learning in Python
tensorflow - An Open Source Machine Learning Framework for Everyone
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
HotBits Python API - Python API for HotBits random data generator
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]