Search Results for "algorithms" - Page 2
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Showing 45 open source projects for "algorithms"
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Behaviour Suite Reinforcement Learning
bsuite is a collection of carefully-designed experiments
bsuite is a research framework developed by Google DeepMind that provides a comprehensive collection of experiments for evaluating the core capabilities of reinforcement learning (RL) agents. Its main goal is to identify, measure, and analyze fundamental aspects of learning efficiency and generalization in RL algorithms. The library enables researchers to benchmark their agents on standardized tasks, facilitating reproducible and transparent comparisons across different approaches. Each experiment in bsuite is meticulously designed to capture key challenges in RL, such as exploration, credit assignment, and stability. The framework supports automated logging and analysis, generating standardized output compatible with Jupyter notebooks for streamlined evaluation. ... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python... -
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
...With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
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Higher
higher is a pytorch library
...It also provides differentiable implementations of common optimizers like SGD and Adam, making it possible to backpropagate through an arbitrary number of inner-loop optimization steps. By offering a clear and flexible interface, higher simplifies building complex learning algorithms that require gradient tracking across multiple update levels. Its design ensures compatibility with existing PyTorch models. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
...At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
...It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
...The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model. -
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That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer... -
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That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.
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Modules for developing, configuring and running a computation based on function blocks entirely in Python. Function block based computation is a data, event and state driven approach to data processing.
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Fast cython implementation of trie data structure for Python. Development is inactive, but moved to: http://github.com/martinkozak/cytrie.
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A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.
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golib
C++ collection mostly for image processing
libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. If...
