QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Overview

QuickAI logo

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Anouncement video https://www.youtube.com/watch?v=kK46sJphjIs

Motivation

When I started to get into more advanced Machine Learning, I started to see how these famous neural network architectures(such as EfficientNet), were doing amazing things. However, when I tried to implement these architectures to problems that I wanted to solve, I realized that it was not super easy to implement and quickly experiment with these architectures. That is where QuickAI came in. It allows for easy experimentation of many model architectures quickly.

Dependencies:

Tensorflow, PyTorch, Sklearn, Matplotlib, Numpy, and Hugging Face Transformers. You should install TensorFlow and PyTorch following the instructions from their respective websites.

Why you should use QuickAI

QuickAI can reduce what would take tens of lines of code into 1-2 lines. This makes fast experimentation very easy and clean. For example, if you wanted to train EfficientNet on your own dataset, you would have to manually write the data loading, preprocessing, model definition and training code, which would be many lines of code. Whereas, with QuickAI, all of these steps happens automatically with just 1-2 lines of code.

The following models are currently supported:

  1. Image Classification

    • EfficientNet B0-B7
    • VGG16
    • VGG19
    • DenseNet121
    • DenseNet169
    • DenseNet201
    • Inception ResNet V2
    • Inception V3
    • MobileNet
    • MobileNet V2
    • MobileNet V3 Small & Large
    • ResNet 101
    • ResNet 101 V2
    • ResNet 152
    • ResNet 152 V2
    • ResNet 50
    • ResNet 50 V2
    • Xception
  2. Natural Language Processing

    • GPT-NEO 125M(Generation, Inference)
    • GPT-NEO 350M(Generation, Inference)
    • GPT-NEO 1.3B(Generation, Inference)
    • GPT-NEO 2.7B(Generation, Inference)
    • Distill BERT Cased(Q&A, Inference and Fine Tuning)
    • Distill BERT Uncased(Named Entity Recognition, Inference)
    • Distil BART (Summarization, Inference)
    • Distill BERT Uncased(Sentiment Analysis & Text/Token Classification, Inference and Fine Tuning)

Installation

pip install quickAI

How to use

Please see the examples folder for details.

Comments
  • Memory error

    Memory error

    Is it possible to host the gpt neo models on a website and make some kind of API, the models are to large to run on my computer. Also It would be nice if to have a stop function so the model knows at what token to stop and be able to add examples of the query needed.

    enhancement 
    opened by TheProtaganist 5
  • Add link to a demo

    Add link to a demo

    Hi, I tried using the notebook in the example folder but it wasn't working (I think the files were not imported into Colab), so I created a demo which should work.

    opened by equiet 1
  • Better code for image_classification.py

    Better code for image_classification.py

    Main change: Used a dict instead of excessive elifs. Other smaller changes.

    Important: I do not have the resources to test the code, but technically, it's just a rewrite of the original, so it should work.

    opened by pinjuf 1
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires numpy, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade ubuntu from 21.10 to jammy

    [Snyk] Security upgrade ubuntu from 21.10 to jammy

    This PR was automatically created by Snyk using the credentials of a real user.


    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:jammy, as this image has only 10 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 514 | Out-of-bounds Read
    SNYK-UBUNTU2110-E2FSPROGS-2770726 | No Known Exploit | | medium severity | 300 | NULL Pointer Dereference
    SNYK-UBUNTU2110-KRB5-1735754 | No Known Exploit | | medium severity | 300 | OS Command Injection
    SNYK-UBUNTU2110-OPENSSL-2933132 | No Known Exploit | | medium severity | 300 | Inadequate Encryption Strength
    SNYK-UBUNTU2110-OPENSSL-2941384 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by geekjr 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from rolling to 21.10

    [Snyk] Security upgrade ubuntu from rolling to 21.10

    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:21.10, as this image has only 12 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Issue | Exploit Maturity | | :------: | :---- | :--------------- | | medium severity | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | NULL Pointer Dereference
    SNYK-UBUNTU2110-TAR-1744334 | No Known Exploit | | medium severity | CVE-2018-25032
    SNYK-UBUNTU2110-ZLIB-2433596 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from 18.04 to rolling

    [Snyk] Security upgrade ubuntu from 18.04 to rolling

    Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:rolling, as this image has only 13 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU1804-PERL-1930908 | No Known Exploit | | low severity | 150 | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU1804-SHADOW-306209 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 471/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.19.5 -> 1.22.0
    | No | No Known Exploit low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.1 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 441/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.1 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3113904 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
Releases(2.0.0)
Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET Can a machine learning project be implemented to estimate the salaries of baseball players whos

Pinar Oner 7 Dec 18, 2021
Repository for DCA0305, an undergraduate course about Machine Learning Workflows and Pipelines

Federal University of Rio Grande do Norte Technology Center Department of Computer Engineering and Automation Machine Learning Based Systems Design Re

Ivanovitch Silva 81 Oct 18, 2022
2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Fluid Simulation Usage Download this repo and store it in your computer. Open a terminal and go to the root directory of this folder. Make sure you ha

Mariana Ávalos Arce 5 Dec 02, 2022
A Python implementation of the Robotics Toolbox for MATLAB

Robotics Toolbox for Python A Python implementation of the Robotics Toolbox for MATLAB® GitHub repository Documentation Wiki (examples and details) Sy

Peter Corke 1.2k Jan 07, 2023
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. Documenta

2.5k Jan 07, 2023
A library of sklearn compatible categorical variable encoders

Categorical Encoding Methods A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques

2.1k Jan 07, 2023
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
Code base of KU AIRS: SPARK Autonomous Vehicle Team

KU AIRS: SPARK Autonomous Vehicle Project Check this link for the blog post describing this project and the video of SPARK in simulation and on parkou

Mehmet Enes Erciyes 1 Nov 23, 2021
Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

Olá! Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogr

Henrique de Paula 10 Apr 04, 2022
Sequence learning toolkit for Python

seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp

Lars 653 Dec 27, 2022
List of Data Science Cheatsheets to rule the world

Data Science Cheatsheets List of Data Science Cheatsheets to rule the world. Table of Contents Business Science Business Science Problem Framework Dat

Favio André Vázquez 11.7k Dec 30, 2022
Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.

Python Extreme Learning Machine (ELM) Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.

Augusto Almeida 84 Nov 25, 2022
Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark environment.

pyspark-anonymizer Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark envir

6 Jun 30, 2022
A flexible CTF contest platform for coming PKU GeekGame events

Project Guiding Star: the Backend A flexible CTF contest platform for coming PKU GeekGame events Still in early development Highlights Not configurabl

PKU GeekGame 14 Dec 15, 2022
Extreme Learning Machine implementation in Python

Python-ELM v0.3 --- ARCHIVED March 2021 --- This is an implementation of the Extreme Learning Machine [1][2] in Python, based on scikit-learn. From

David C. Lambert 511 Dec 20, 2022
决策树分类与回归模型的实现和可视化

DecisionTree 决策树分类与回归模型,以及可视化 DecisionTree ID3 C4.5 CART 分类 回归 决策树绘制 分类树 回归树 调参 剪枝 ID3 ID3决策树是最朴素的决策树分类器: 无剪枝 只支持离散属性 采用信息增益准则 在data.py中,我们记录了一个小的西瓜数据

Welt Xing 10 Oct 22, 2022
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022
This repository demonstrates the usage of hover to understand and supervise a machine learning task.

Hover Example Apps (works out-of-the-box on Binder) This repository demonstrates the usage of hover to understand and supervise a machine learning tas

Pavel 43 Dec 03, 2021
DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

27 Aug 19, 2022