Artificial-Intelligence_resources-and-notebooks
tensorflow
Artificial-Intelligence_resources-and-notebooks | tensorflow | |
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2 | 226 | |
7 | 183,409 | |
- | 0.5% | |
0.0 | 10.0 | |
about 4 years ago | 2 days ago | |
Jupyter Notebook | C++ | |
Apache License 2.0 | 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.
Artificial-Intelligence_resources-and-notebooks
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Get started with TensorFlow and Deep Learning
If you are using Jupyter Notebooks in your local environment download the code file here.
tensorflow
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Top 17 Fast-Growing Github Repo of 2024
TensorFlow
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Awesome List
GitHub Repository - The main TensorFlow repository.
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Rebuilding TensorFlow 2.8.4 on Ubuntu 22.04 to patch vulnerabilities
The official 2.8.4 container was published in Nov 2022. That's 1.5 years of OS updates at least. I looked up the 2.8.4 source and found that it's using Ubuntu 20.04 as the base OS. Of note, we're using the x86_64 architecture according to the container image layer: ENV NVARCH=x86_64.
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
What are some alternatives?
docs - TensorFlow documentation
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
Deep-Learning-With-TensorFlow-Blog-series - All the resources and hands-on exercises for you to get started with Deep Learning in TensorFlow [Moved to: https://github.com/Rishit-dagli/Deep-Learning-With-TensorFlow]
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Deep-Learning-With-TensorFlow - All the resources and hands-on exercises for you to get started with Deep Learning in TensorFlow
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
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
scikit-learn - scikit-learn: machine learning in Python
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
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
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration