Keras
tensorflow
Our great sponsors
Keras | tensorflow | |
---|---|---|
65 | 189 | |
57,203 | 170,805 | |
0.4% | 0.5% | |
9.6 | 10.0 | |
2 days ago | 2 days ago | |
Python | 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.
Keras
-
How to query pandas DataFrames with SQL
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.
-
The Essentials of a Contributor-friendly Open-source Project
Our trick is to support GitHub Codespaces, which provides a web-based Visual Studio Code IDE. The best thing is you can specify a Dockerfile with all the required dependency software installed. With one click on the repo’s webpage, your contributors are ready to code. Here is our setup for your reference.
-
DO YOU YAML?
If you’re looking for further resources on running TensorFlow and Keras on a newer MacBook, I recommend checking out this YouTube video: How to Install Keras GPU for Mac M1/M2 with Conda
-
Doing k-fold analysis
The thing that pops right into my mind is the following issue: https://github.com/keras-team/keras/issues/13118 People are still reporting memory leaks when calling model.predict and I wouldn't be surprised if model.fit also leaked when called multiple times. Maybe this is a good starting point for your investigation. If this is unrelated, I'm sorry in forward.
-
65 Blog Posts to Learn Data Science
Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start.
-
Инструменты Python. Библиотеки для анализа данных
- statsmodel (https://keras.io);
-
Keras vs Tensorflow vs Pytorch for a Final year Project
E.g. If you consider it image classification (you already have the pedestrians extracted and just need to classify their intent), you might find that easier to do with Keras, just butcher one of the examples on keras.io. You might also find fast.ai more to your liking.
-
A few (unordered) thoughts about data (1/2)
Keras
-
How to Build a Machine Learning Recommendation Engine w/ TensorFlow & HarperDB
This is where machine learning takes over. Using libraries such as TensorFlow Recommenders with Keras models, it's easy to shape the data in ways that will allow the items and users to be viewed and compared in a multidimensional perspective. Qualitative features such as item categories and user profile attributes can be mapped into mathematical concepts that can be quantitatively compared with one another, ultimately providing new insights and better recommendations.
-
Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
Keras – An open-source software library that provides a Python interface to TensorFlow for artificial neural networks
tensorflow
-
How worried are you about AI taking over music?
Tensorflow 238k contributors
-
Python's "Disappointing" Superpowers
C++ is actually used in machine learning. More than 60% of TensorFlow code is in C++: https://github.com/tensorflow/tensorflow. With high level configs and prototyping is done in python.
- 👙 Ready for Thot or Bot? 🤖
-
10 Interesting GitHub Repos Worth Checking Out
10. Tensorflow
-
Data-Oriented Programming in Python
> In practice, scientific computing users rely on the NumPy family of libraries e.g. NumPy, SciPy, TensorFlow, PyTorch, CuPy, JAX, etc..
this is a somewhat confusing statement. most of these libraries actually don't rely on numpy. e.g. tensorflow ultimately wraps c++/eigen tensors [0] and numpy enters somewhere higher up in their python integration
[0] https://github.com/tensorflow/tensorflow/blob/master/tensorf...
-
Anyone attempted to convert stablediffusion tensorflow to tf lite?
``` Downloading data from https://github.com/openai/CLIP/blob/main/clip/bpe\_simple\_vocab\_16e6.txt.gz?raw=true 1356917/1356917 [==============================] - 0s 0us/step WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23. Instructions for updating: Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089 By using this model checkpoint, you acknowledge that its usage is subject to the terms of the CreativeML Open RAIL-M license at https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv\_encoder.h5 492466864/492466864 [==============================] - 7s 0us/step Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv\_diffusion\_model.h5 3439090152/3439090152 [==============================] - 85s 0us/step Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv\_decoder.h5 198180272/198180272 [==============================] - 3s 0us/step ``` I attempted to save the model from Keras_cv but it throws the same error.
-
Does anyone feel like R is actually vastly worse for dependency/environment management than Python?
Python 3.11 is worse. It doesn't even support Tensorflow (at least as of now), which is arguably the most popular deep learning package in Python.
-
Elon Musk dissolves Twitter's board of directors
So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.
-
TF2.11 dropping official native support for Windows?
It seems like Tensorflow 2.11 is dropping official native support for Windows. I was trying to compile TF 2.11-rc1 on Windows but hit with an error which I have reported here (https://github.com/tensorflow/tensorflow/issues/58323). They told me to refer to the release note (https://github.com/tensorflow/tensorflow/blob/v2.11.0-rc1/RELEASE.md#major-features-and-improvements)
- ML Frameworks
What are some alternatives?
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)
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
scikit-learn - scikit-learn: machine learning in Python
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.
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
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
PyBrain
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
MLflow - Open source platform for the machine learning lifecycle
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration