redframes
TFLearn
redframes | TFLearn | |
---|---|---|
10 | 2 | |
295 | 9,605 | |
- | 0.0% | |
1.4 | 0.0 | |
about 1 year ago | 5 months ago | |
Python | Python | |
BSD 2-clause "Simplified" License | MIT License |
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redframes
- What is something you wish there was a Python module for?
- Redframes: General Purpose Data Manipulation Library
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
I'm not GP, but I find the pandas API incredibly inconsistent and difficult to remember how to do simple transformations. For example, it sometimes overloads operators because it doesn't use built in language features like lambdas. There are reasons for the inconsistency, but using the alternatives like R's tidyverse or Julia's DataFramess.jl is like night and day for me.
I found RedFrames [1] recently which wraps Pandas dataframes with a more consistent interface, it's probably what I'd use if I had to write data transformations that had to be compatible with Pandas.
[1] https://github.com/maxhumber/redframes
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Ask HN: How you maintain your daily log?
[2022-10-23 14:11:15]: Question []: should we use Red Frames (https://github.com/maxhumber/redframes) in addition to Pandas? Criteria for decision? @me #projectLion
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Python 3.11.0 final is now available
If you like writing chain-able pandas, you should check out: https://github.com/maxhumber/redframes
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Add your own custom methods to third-party types with this pattern
I intend to use this pattern in my redframes library to hijack some pd.DataFrame methods.
- GitHub - maxhumber/redframes: [re]ctangular[d]ata[frames]
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Ask HN: What are you doing this weekend?
I'm dog-fooding my new Python data manipulation library, redframes: https://github.com/maxhumber/redframes
To help me prep for my Fantasy Hockey Draft next week!
- redframes, a new data manipulation library for ML and visualization
- Show HN: Redframes, a Python data manipulation library like dplyr
TFLearn
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
TFLearn – Deep learning library featuring a higher-level API for TensorFlow
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Base ball
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
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
Keras - Deep Learning for humans
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tensorflow - An Open Source Machine Learning Framework for Everyone
scikit-learn - scikit-learn: machine learning in Python
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
MLflow - Open source platform for the machine learning lifecycle
tfgraphviz - A visualization tool to show a TensorFlow's graph like TensorBoard