Keras
Prophet
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Keras | Prophet | |
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65 | 179 | |
57,203 | 15,462 | |
0.4% | 1.0% | |
9.6 | 6.5 | |
7 days ago | 9 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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
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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.
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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.
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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
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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.
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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.
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Инструменты Python. Библиотеки для анализа данных
- statsmodel (https://keras.io);
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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.
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A few (unordered) thoughts about data (1/2)
Keras
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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.
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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
Prophet
- Dec 12, 2022 FLiP Stack Weekly
- Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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[D] Time Series Question
Prophet
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LSTM/CNN architectures for time series forecasting[Discussion]
Prophet
- Eden
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Predição de ações na bolsa de valores com Python e Facebook Prophet
Prophet: Automação preditiva.
- Time series analysis of Bitcoin price in Python with fbprophet ?!
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Data Science toolset summary from 2021
Prophet - It is a time-series forecasting library built by Facebook. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Link - https://github.com/facebook/prophet
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Personal Support at Internet Scale
We run an anomaly detection app powered by Facebook's Prophet forecasting library. It tells us if metrics dip or rise in unexpected ways ("Did signups drop? Is something broken with that flow?"). We built the service because customers kept reaching out to tell us some feature broke before we noticed. Normally these issues show up in product data, so the app looks for these anomalies and tells us when they happen.
- Discussion Thread
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
scikit-learn - scikit-learn: machine learning in Python
darts - A python library for user-friendly forecasting and anomaly detection on time series.
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
greykite - A flexible, intuitive and fast forecasting library
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
MLflow - Open source platform for the machine learning lifecycle
sktime - A unified framework for machine learning with time series