Neural-network Libraries

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Neural-network Libraries

An Open Source Machine Learning Framework for Everyone

TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the…

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Use any of the following services. Next, run the following commands: Finally, start Jupyter: I recommend Python 3.7. If you follow the installation instructions above, that's the version you will get…

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

If you prefer to use latest CNTK bits from master, use one of the CNTK nightly packages: You can learn more about using and contributing to CNTK with the following resources: Dear community, With our…

⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡

Input encrypted text, get the decrypted text back. "What type of encryption?" That's the point. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can so…

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

To install the current release: Participating discussions via the following IM groups is also welcomed. Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull req…

The "Python Machine Learning (1st edition)" book code repository and info resource

What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into…

The Unified Machine Learning Framework

Ivy is both an ML transpiler and a framework, currently supporting JAX, TensorFlow, PyTorch and Numpy. Ivy's transpiler allows you to use code from any other framework (or from any other version of t…

Low-code framework for building custom LLMs, neural networks, and other AI models

Key features: Install from PyPi. Be aware that Ludwig requires Python 3.8+. Or install with all optional dependencies: Let's fine-tune a pretrained LLaMA-2-7b large language model to follow instructi…

A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley II, 2018.

Join our Telegram discussion group to ask any questions you have about Video2X, chat directly with the developers, or discuss about upscaling technologies and the future of Video2X in general. The fo…

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

An open-source python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. We t…

A flexible, high-performance serving system for machine learning models

To note a few features: The easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not address…

Deep learning with satellite & aerial imagery

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment In instance segmentation, each individual 'instance' of a segmented area is given a u…

Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer gr…

😎 Finding duplicate images made easy!

This package provides functionality to make use of hashing algorithms that are particularly good at finding exact duplicates as well as convolutional neural networks which are also adept at finding n…

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Plain python implementations of basic machine learning algorithms

If you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue. See the LICENSE file for license rights and limitations…

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

IPython Notebook(s) and other programming tools such as Torch/Lua/D lang in demonstrating deep learning functionality. Additional PyRo tutorials: IPython Notebook(s) demonstrating scikit-learn functi…

🛠 All-in-one web-based IDE specialized for machine learning and data science.

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on you…

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

You can start with this article that I wrote in Heartbeat magazine (on Medium platform): Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, …

Data Science Roadmap from A to Z

Pandas

StellarGraph - Machine Learning on Graphs

Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can…

Carefully curated resource links for data science in one place

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place. Homework 3

Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

Then, define the BigDL model using Keras-style API:

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

This repository contains all the code, notebooks and examples used in this book. We will also be adding bonus content here from time to time. So keep watching this space! Master the essential skills …

A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!

A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models! The concept of AI-Blocs is to have a simple scene with draggable objects that have scripts attached to…

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Install PyGAD with the following command: The source code of the PyGAD' modules is found in the following GitHub projects: The next code implements all the callback functions to trace the execution o…

Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

We believe mastery of a certain machine learning concept/topic is achieved only when the answer to each of the following three questions is affirmative. Example ”roadmaps” shown below provide suggest…

Multi-class confusion matrix library in Python

Fig1. ConfusionMatrix Block Diagram After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of seven overall benchmar…