Search Results for "machine learning predictive" - Page 21
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Showing 664 open source projects for "machine learning predictive"
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AdaNet
Fast and flexible AutoML with learning guarantees
AdaNet is a TensorFlow framework for fast and flexible AutoML with learning guarantees. AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture but also for learning to the ensemble to obtain even better models. At each... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,... -
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NSFW Detection Machine Learning Model
Keras model of NSFW detector
Keras model of NSFW detector, NSFW Detection Machine Learning Model. -
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Delta ML
Deep learning based natural language and speech processing platform
DELTA is a deep learning-based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train,... -
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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 compare the results. ... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. 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... -
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Magnitude
A fast, efficient universal vector embedding utility package
A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim but can be used as a generic key-vector store for domains outside NLP. It offers unique features like out-of-vocabulary lookups and streaming of large models over HTTP. Published in our paper at EMNLP 2018 and available on arXiv. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. -
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COCO Annotator
Web-based image segmentation tool for object detection & localization
COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, label objects with disconnected visible parts, and efficiently store and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and... -
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cintruder
CIntruder - OCR Bruteforcing Toolkit
Captcha Intruder is an automatic pentesting tool to bypass captchas. -> CIntruder-v0.4 (.zip) -> md5 = 6326ab514e329e4ccd5e1533d5d53967 -> CIntruder-v0.4 (.tar.gz) ->md5 = 2256fccac505064f3b84ee2c43921a68 -------------------------------------------- -
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Spectral Python
A python module for hyperspectral image processing
Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. -
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NLP Best Practices
Natural Language Processing Best Practices & Examples
...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems. -
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Knock Knock
Get notified when your training ends
Knock Knock is a lightweight Python utility created by the Hugging Face team that allows developers to receive notifications when long-running machine learning tasks finish or fail. Training deep learning models often takes hours or even days, making it inconvenient for engineers to constantly monitor progress manually. The library solves this problem by adding simple decorators or command-line commands that automatically send notifications when a process completes or crashes. ... -
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Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
Machine Learning with TensorFlow is an open repository containing the source code and practical examples that accompany the book Machine Learning with TensorFlow. The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. ... -
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Top deep learning Github repositories
Top 200 deep learning Github repositories sorted by stars
Top-Deep-Learning is a curated repository that aggregates some of the most influential and widely used deep learning projects available on GitHub. Instead of providing its own machine learning models or frameworks, the project functions as an organized index that helps users discover high-quality deep learning repositories across different application domains. -
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TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook repository provides practical code examples and educational materials that accompany the book TensorFlow Machine Learning Cookbook. The repository contains numerous Python scripts and Jupyter notebooks that demonstrate how to implement machine learning algorithms and neural networks using the TensorFlow framework. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0). Applicability. Many people already have their own ML workflows and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes. Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and... -
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NLP-progress
Repository to track the progress in Natural Language Processing (NLP)
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets. It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such... -
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CrypTen
A framework for Privacy Preserving Machine Learning
CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. ... -
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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. Environments... -
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Spinning Up in Deep RL
Educational resource to help anyone learn deep reinforcement learning
Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar, reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning. At OpenAI, we believe that deep learning generally, and deep reinforcement learning specifically, will play central roles in the development of powerful AI technology. ... -
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imgaug
Image augmentation for machine learning experiments
imgaug is a library for image augmentation in machine learning experiments. It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images but also key points/landmarks, bounding boxes, heatmaps and segmentation maps. Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, etc. ... -
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BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. In certain scenarios, BytePS can double the training speed compared with Horovod+NCCL.... -
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Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles.
