Search Results for "source code computer" - Page 3
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136 projects for "source code computer" with 2 filters applied:
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
SimpleHTR is an open-source implementation of a handwriting text recognition system based on deep learning techniques. The project focuses on converting images of handwritten text into machine-readable digital text using neural networks. The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting. -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
Machine learning basics repository is an educational project that provides plain Python implementations of fundamental machine learning algorithms designed to help learners understand how these methods work internally. Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such... -
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GPU Puzzles
Solve puzzles. Learn CUDA
GPU Puzzles is an educational project designed to teach GPU programming concepts through interactive coding exercises and puzzles. Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively... -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ... -
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Advanced NLP with spaCy
Advanced NLP with spaCy: A free online course
...It also demonstrates how spaCy pipelines work and how developers can extend them with custom components and training data. The course is structured as a hands-on learning environment where students can run code examples, experiment with NLP techniques, and build practical language processing applications. Because spaCy is widely used in production environments, the course emphasizes industrial-strength NLP workflows and best practices. -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. -
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seq2seq-couplet
Play couplet with seq2seq model
...The repository also points users to an external dataset source and documents vocabulary formatting requirements for custom datasets, showing that it is meant for both experimentation and extension. -
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Made With ML
Learn how to develop, deploy and iterate on production-grade ML
Made-With-ML is an open-source educational repository and course designed to teach developers how to build production-grade machine learning systems using modern MLOps practices. The project focuses on bridging the gap between experimental machine learning notebooks and real-world software systems that can be deployed, monitored, and maintained at scale. It provides structured lessons and practical code examples that demonstrate how to design machine learning workflows, manage datasets, train models, evaluate performance, and deploy inference services. ... -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. ... -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
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crème de la crème of AI courses
This repository is a curated collection of links to various courses
...Topics covered include deep learning, natural language processing, computer vision, large language models, linear algebra, reinforcement learning, and machine learning engineering. Because the repository links to well-known educational content such as university lecture series and professional training materials, it functions as a structured roadmap for individuals who want to develop expertise in artificial intelligence. -
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A Survey of Surveys
A collection of 1000+ survey papers on Natural Language Processing
A Survey of Surveys is a large curated repository that collects and organizes survey papers related to natural language processing, machine learning, and artificial intelligence research. The project aims to provide a centralized index of survey literature that summarizes major developments across different subfields of AI. Rather than focusing on code implementations, the repository functions as an academic resource that helps researchers quickly discover comprehensive survey papers covering various topics. These topics include areas such as neural machine translation, language models, computer vision, and deep learning architectures. The repository organizes hundreds of papers into thematic categories and includes references, links, and bibliographic information to facilitate research and literature exploration. -
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Computer vision projects
computer vision projects | Fun AI projects related to computer vision
Computer vision projects is an open-source collection of computer vision projects and experiments that demonstrate practical applications of modern AI techniques in image processing, robotics, and real-time visual analysis. The repository includes multiple demonstration systems implemented using languages such as Python and C++, covering topics ranging from object detection to embedded vision systems. -
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Transformers in Time Series
A professionally curated list of awesome resources
Transformers in Time Series is a curated research repository that collects academic papers, code implementations, datasets, and learning resources related to transformer models for time series analysis. The project was created to systematically organize the rapidly growing research field that applies transformer architectures to time series modeling tasks. It compiles literature from major conferences and journals and categorizes them by application domains such as forecasting, anomaly... -
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OpenNN - Open Neural Networks Library
Machine learning algorithms for advanced analytics
OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention…), health care (early diagnosis, microarray analysis…) and engineering (performance optimization, predictive maitenance…). OpenNN does not deal with computer vision or natural language processing. The main advantage of OpenNN is its... -
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AI-Aimbot
CS2, Valorant, Fortnite, APEX, every game
AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The... -
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ICCV2023-Paper-Code-Interpretation
ICCV2021/2019/2017 Paper/Code/Interpretation/Live Broadcast Collection
ICCV2023-Paper-Code-Interpretation is a curated repository that provides explanations and interpretations of code associated with research papers presented at the International Conference on Computer Vision (ICCV) 2023. The project focuses on helping researchers and students better understand how complex computer vision algorithms described in academic papers are implemented in practice. -
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The Algorithms - C #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C is an open-source repository that provides implementations of classic algorithms and data structures written in the C programming language. The project is part of the larger “The Algorithms” initiative, which aims to create educational resources by implementing algorithms in multiple programming languages. Within the C repository, contributors implement algorithms from many areas of computer science including sorting, searching, graph processing, mathematics, machine learning, and numerical methods. ... -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
...Each notebook introduces theoretical ideas and then demonstrates how to implement them using Python libraries commonly used in data science, such as NumPy, pandas, scikit-learn, and TensorFlow. The repository also includes examples related to natural language processing, computer vision, and data visualization, giving learners exposure to several subfields of machine learning. By organizing the content into modular notebooks, the project allows users to explore topics independently and experiment with the code directly. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common... -
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Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
...Wireless communication research has increasingly adopted deep learning techniques to address complex tasks such as channel estimation, resource allocation, signal detection, and modulation classification. However, many academic publications do not release source code, which makes it difficult for new researchers to reproduce results or experiment with the proposed methods. This repository addresses that challenge by organizing a large set of papers and linking them to available implementations and related research resources. -
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fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. ... -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation...
