Deep-learning Libraries

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Deep-learning Libraries

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Here you find my IPython (Jupyter) notebooks from my blog (ipythonquant.wordpress.org). Feel free to download the samples and play around with them.

A toolkit for making real world machine learning and data analysis applications in C++

Go into the examples folder and type: That will build all the examples. If you have a CPU that supports AVX instructions then turn them on like this: Doing so will make some things run faster. Finall…

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.

GitHub GitHub issues -- file bug reports / track issues here Stack Overflow -- ask all code/software questions here Cross Validated (Stack Exchange) -- ask algorithm/theory questions here h2ostream G…

A framework for high-performance medical image processing, neural network inference and visualization

FAST is an open-source framework with the main goal of making it easier to do high-performance processing, neural network inference, and visualization of medical images utilizing multi-core CPUs and …

A collection of machine learning examples and tutorials.

A collection of machine learning examples and tutorials. Please note that not all code from all courses will be found in this repository. Some newer code examples (e.g. most of Tensorflow 2.0) were d…

Machine Learning Platform and Recommendation Engine built on Kubernetes

Seldon Server is a machine learning platform that helps your data science team deploy models into production. Seldon supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and a…

Interactive Image similarity and Visual Search and Retrieval application

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

The algorithms studied are in various commonly used open source implementations like In summary, we are focusing on which algos/implementations can be used to train relatively accurate binary classif…

:earth_americas: machine learning tutorials (mainly in Python3)

This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics. Curated notes on deep learning. Notes related to advertising domai…

Tutorials and training material for the H2O Machine Learning Platform

This document contains tutorials and training materials for H2O-3. If you find any problems with the tutorial code, please open an issue in this repository. There are a number of tutorials on all so…

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…

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…

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…

Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost

You should already have pip, Python, and optionally XGBoost, LightGBM, and CatBoost installed on your system (see below). Run the following command to install AlphaPy: Pyfolio is automatically instal…

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…

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…

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

There are options: Below shows how to build the standalone executable on macOS, Linux and Windows.

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

There are options: A few object categories and images: Below shows how to build the standalone executable on macOS, Linux and Windows.

Super Resolution of picture images using deep learning

Super Resolution of picture images using deep learning. SeRanet upscales picture image size to x2. Before upscale Conventional method: image upscaled with lanczos method (with OpenCV library) Origina…

U-Net: Convolutional Networks for Biomedical Image Segmentation

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I develo…

Setup Ubuntu 18.04, 16.04 and 14.04 with machine learning and robotics software plus user configuration. Includes ceres tensorflow ros caffe vrep eigen cudnn and cuda plus many more.

This contains a set of linux setup scripts consolidated from across the web to make it easy to set up a new computer for robotics and deep learning, with links to the original sources and brief descr…

[UNMAINTAINED] Automated machine learning for analytics & production

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

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…

Tool for reading and writing datasets of tensors in a Lightning Memory-Mapped Database (LMDB). Designed to manage machine learning datasets with fast reading speeds.

The installation instructions are generic and should work on most operating systems that support the prerequisites. Please refer to the individual packages for more information about additional depen…

Python suite to construct benchmark machine learning datasets from the MIMIC-III 💊 clinical database.

Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database. Currently, the benchmark datasets cover four key inpatient clinical prediction tasks that map onto …

Solutions to assignments of Coursera Specializations - Deep learning, Machine learning, Algorithms & Data Structures, Image Processing and Python For Everybody

Solutions to assignments of the following Specializations on Coursera: NOTE: Please ★ the repo if you find it useful.

Test implementation of Deeply-Recursive Convolutional Network for Image Super-Resolution

Training highly deep CNN layers is so hard. However this paper makes it with some tricks like sharing filter weights and using intermediate outputs to suppress divergence in training. The model in th…

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