deep_learning_and_the_game_of_go
Keras
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| deep_learning_and_the_game_of_go | Keras | |
|---|---|---|
| 3 | 78 | |
| 929 | 60,937 | |
| - | 0.6% | |
| 0.0 | 9.9 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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deep_learning_and_the_game_of_go
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Training an AI for Tigris and Euphrates
A good book I found is https://www.manning.com/books/deep-learning-and-the-game-of-go
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Why do engines often evaluate completely winning endgame positions between +60 and +63? What's significant about the low 60's as an evaluation? Or is it just a placeholder when the computer can't quite find a forced mate?
If you want to understand how the new approach used by Leela Zero and Alpha Zero works, the book Deep Learning and the Game of Go is fun and easy to read. Although it's about Go rather than chess, most of the contents are equally relevant to chess.
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[Q] Deep Learning and the Game of Go - anyone got the code to work?
One of the first hits pointed me to this github repo: https://github.com/maxpumperla/deep_learning_and_the_game_of_go
Keras
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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
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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?
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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.
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List of AI-Models
Click to Learn more...
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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?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
deepxde - A library for scientific machine learning and physics-informed learning
scikit-learn - scikit-learn: machine learning in Python
GeneticAlgorithmPython - Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
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
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
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
pycox - Survival analysis with PyTorch
tensorflow - An Open Source Machine Learning Framework for Everyone
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.