Keras
scikit-learn
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Keras | scikit-learn | |
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65 | 61 | |
57,203 | 52,699 | |
0.4% | 0.5% | |
9.6 | 9.9 | |
1 day ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" 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
scikit-learn
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Best Websites For Coders
Scikit-learn : A Python module for machine learning build on top of SciPy
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scikit-learn VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
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Boston Dataset was Removed from scikit-learn 1.2
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
- ML Frameworks
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Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide.
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How do you programmers make sense of production-level code?
If you look at the README for scikit-learn on GitHub, they say this.
Take a smaller segment to look at. Opening up the front page to a Github repo can be quite daunting. https://github.com/scikit-learn/scikit-learn
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Talking Data: What do we need for engaging data analytics?
Many data workers are complaining about the fierce competition in the data area. Fortunately, the situation seems to be improving. Data analysts had to manually analyze distribution charts for deep insights, but now they can use smart machine learning models to automate this process. Traditional data analysis and modeling skills have been gradually becoming easy. For instance, Power BI or Tableau allow users to use a drag-and-drop low-code fashion to generate visual charts and models, whilst the old way is to import Python libraries such as pandas, matplotlib and sklearn to do the same in Jupyter Notebook. Open-source projects Apache Superset and Metabase allow users to easily analyze data on the web pages. This is quite similar to the development of digital cameras, from the film cameras to digital cameras and to smartphone cameras used by everyone. With lower and lower technical barriers, the whole industry can be developing fast. "Everyone can be data analyst" will no longer be a fantasy.
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A few (unordered) thoughts about data (1/2)
scikit-learn
What are some alternatives?
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Surprise - A Python scikit for building and analyzing recommender systems
tensorflow - An Open Source Machine Learning Framework for Everyone
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
gensim - Topic Modelling for Humans
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
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
PyBrain