Search Results for "machine learning python" - Page 60
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Showing 1970 open source projects for "machine learning python"
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
DS-Take-Home is a repository that provides practical solutions to a series of real-world data science challenges inspired by the book A Collection of Data Science Take-Home Challenges. The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing datasets, performing exploratory data analysis, building predictive models, and interpreting results. ... -
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lazynlp
Library to scrape and clean web pages to create massive datasets
LazyNLP is a lightweight tool for collecting and curating large-scale text datasets for machine learning and NLP applications with minimal manual effort. -
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Machine learning Resources
Some learning materials and research introduction on machine learning
Machine learning Resources is an educational GitHub repository that collects resources, tutorials, and implementation examples related to machine learning theory and practice. The project aims to help learners understand machine learning from both conceptual and practical perspectives by combining explanations, research references, and coding examples. -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not... -
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DeepTraffic
DeepTraffic is a deep reinforcement learning competition
DeepTraffic is a deep reinforcement learning simulation designed to teach and evaluate autonomous driving algorithms in a dense highway environment. The system presents a simulated multi-lane highway where an AI-controlled vehicle must navigate traffic while maximizing speed and avoiding collisions. Participants design neural network policies that determine the vehicle’s actions, such as accelerating, decelerating, changing lanes, or maintaining speed. The project was created as part of an... -
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NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
Lihang is an open-source repository that provides educational notes, mathematical derivations, and code implementations based on the book Statistical Learning Methods by Li Hang. The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector machines, and hidden Markov models work in practice. ... -
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Arabic Corpus
Text categorization, arabic language processing, language modeling
The Arabic Corpus {compiled by Dr. Mourad Abbas ( http://sites.google.com/site/mouradabbas9/corpora ) The corpus Khaleej-2004 contains 5690 documents. It is divided to 4 topics (categories). The corpus Watan-2004 contains 20291 documents organized in 6 topics (categories). Researchers who use these two corpora would mention the two main references: (1) For Watan-2004 corpus ---------------------- M. Abbas, K. Smaili, D. Berkani, (2011) Evaluation of Topic Identification Methods on... -
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BossSensor
Hide screen when boss is approaching
...The software relies on libraries such as OpenCV, TensorFlow, and Python-based machine learning tools to perform face detection and classification. Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them. -
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howmanypeoplearearound
Count the number of people around you by monitoring wifi signals
howmanypeoplearearound calculates the number of people in the vicinity using the approximate number of smartphones as a proxy (since ~70% of people have smartphones nowadays). A cellphone is determined to be in proximity to the computer based on sniffing WiFi probe requests. Possible uses of howmanypeoplearearound include, monitoring foot traffic in your house with Raspberry Pis, seeing if your roommates are home, etc. There are a number of possible USB WiFi adapters that support monitor... -
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LUMINOTH
Deep Learning toolkit for Computer Vision
LUMINOTH is an open-source deep learning toolkit designed for computer vision tasks, particularly object detection. The framework is implemented in Python and built on top of TensorFlow and the Sonnet neural network library, providing a modular environment for training and deploying detection models. It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. -
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Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein applications configure an entire deployment of the system. ... -
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Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months"
This project lays out a 12-week plan to go from basics to a portfolio-ready understanding of data science. It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck wondering what to analyze next. ... -
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Deepvoice3_pytorch
PyTorch implementation of convolutional neural networks
An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
Dynamic Routing Between Capsules is a PyTorch implementation of the Capsule Network architecture originally proposed to address limitations in traditional convolutional neural networks. Capsule networks aim to improve how neural models represent spatial hierarchies and relationships between objects within images. Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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The GAN Zoo
A list of all named GANs
The GAN Zoo is an open-source repository that compiles a comprehensive list of Generative Adversarial Network models published in research literature. The project began as a community effort to track the rapidly growing number of GAN architectures appearing in machine learning papers. Because new GAN models are frequently introduced in research publications, the repository serves as a convenient catalog that organizes them in one location. The list includes references to many GAN variants along with links to their original research papers and sometimes implementation code. Users can browse the dataset or explore a tabular version that allows filtering by year or searching for specific model names. ... -
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TensorImage
Image classification library for easily training and deploying models
(Visit our github repository at https://github.com/TensorImage/tensorimage for more information) TensorImage is and open source package for image classification. It has a wide range of data augmentation operations that can be performed over training data to prevent overfitting and increase testing accuracy. TensorImage is easy to use and manage as all files, trained models and data are organized within a workspace directory, which you can change at any time in the configuration file,... -
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Pragmatic AI
[Book-2019] Pragmatic AI: An Introduction to Cloud-based ML
Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. ... -
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InfoGAN
Code for reproducing key results in the paper
The InfoGAN repository contains the original implementation used to reproduce the results in the paper “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. InfoGAN is a variant of the GAN (Generative Adversarial Network) architecture that aims to learn disentangled and interpretable latent representations by maximizing the mutual information between a subset of the latent codes and the generated outputs. That extra incentive encourages the... -
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3D ResNets for Action Recognition
3D ResNets for Action Recognition (CVPR 2018)
We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time. We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch. -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
...Install Docker and Docker Compose. Edit envs/docker-compose.yaml according to your environment. Then, launch containers by docker-compose. We recommend to use pipenv to make a Python environment for this project. Suppose you have a database named master on your local machine. Prediction submodule generates a single comment of a financial instrument at specified time by loading a trained model. -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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Meta-Learning-Papers
Meta Learning/Learning to Learn/One Shot Learning/Few Shot Learning
Meta-Learning-Papers is a curated bibliography focused specifically on meta-learning, learning-to-learn, one-shot learning, and few-shot learning, intended for researchers and practitioners interested in this rapidly evolving subfield of machine learning. It catalogs foundational “legacy” papers that introduced key concepts, as well as more recent work that extends meta-learning to new domains or architectures. -
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EECluster
Tool for energy-efficient resource management in HPC clusters
EECluster is software tool for managing the energy-efficient allocation of the cluster resources. EECluster uses a Hybrid Genetic Fuzzy System as the decision-making mechanism that elicits part of its rule base dependent on the cluster workload scenario, delivering good compliance with the administrator preferences. In the latest version, we leverage a more sophisticated and exhaustive model that covers a wider range of environmental aspects and balances service quality and power...
