Search Results for "learning" - Page 41
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Showing 2456 open source projects for "learning"
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides... -
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Burn To The Brim
Utility for efficiently grouping files and folders together
...By recursively scanning your designated folders, BTTB matches item sizes to your media capacity, finding a near-perfect selection in milliseconds and an absolute perfect packing configuration in just a few seconds. Burn to the Brim 4.0.0 introduces state-of-the-art machine learning semantic coherence logic to BTTB's classical packing algorithms. -
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Blazeface
Blazeface is a lightweight model that detects faces in images
...It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed to work in real-world applications like mobile apps, robotics, and other resource-constrained environments. -
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MATLAB Deep Learning Model Hub
Discover pretrained models for deep learning in MATLAB
Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
...It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning points would easily apply to Imagen), make a few minor modifications for attention across time and other ways to skimp on the compute cost, do frame interpolation correctly, get a great video model out. Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. ... -
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MoveNet
A CNN model that predicts human joints from RGB images of a person
The MoveNet model is an efficient, real-time human pose estimation system designed for detecting and tracking keypoints of human bodies. It utilizes deep learning to accurately locate 17 key points across the body, providing precise tracking even with fast movements. Optimized for mobile and embedded devices, MoveNet can be integrated into applications for fitness tracking, augmented reality, and interactive systems. -
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talos
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
...Talos is made for data scientists and data engineers that want to remain in complete control of their TensorFlow (tf.keras) and PyTorch models, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of reducing it. Within minutes, without learning any new syntax, Talos allows you to configure, perform, and evaluate hyperparameter optimization experiments that yield state-of-the-art results across a wide range of prediction tasks. Talos provides the simplest and yet most powerful available method for hyperparameter optimization with TensorFlow (tf.keras) and PyTorch. -
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Universal Sentence Encoder
Encoder of greater-than-word length text trained on a variety of data
The Universal Sentence Encoder (USE) is a pre-trained deep learning model designed to encode sentences into fixed-length embeddings for use in various natural language processing (NLP) tasks. It leverages Transformer and Deep Averaging Network (DAN) architectures to generate embeddings that capture the semantic meaning of sentences. The model is designed for tasks like sentiment analysis, semantic textual similarity, and clustering, and provides high-quality sentence representations in a computationally efficient manner. -
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Data Annotator for Machine Learning
Data annotator for machine learning
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. -
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
...The DeepChem project maintains an extensive collection of tutorials. All tutorials are designed to be run on Google collab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence that will take you from beginner to proficient at molecular machine learning and computational biology more broadly. -
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AIF360
A comprehensive set of fairness metrics for datasets
This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. ... -
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minbpe
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm
...It operates on UTF-8 encoded bytes rather than Unicode characters, which makes it robust to arbitrary text inputs and avoids needing a language-specific character vocabulary. The repository is structured as a teaching-oriented implementation that shows how to train a tokenizer by learning merge rules, then apply those merges to encode text into token IDs and decode tokens back into text. It is intentionally small and readable so developers can understand each stage of BPE, including the mechanics of pair counting, merge application, and vocabulary growth. The project is especially useful for practitioners who want to demystify how LLM tokenizers work or who need a lightweight reference implementation for experimentation. -
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A Survey of Surveys
A collection of 1000+ survey papers on Natural Language Processing
...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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higgsfield
Fault-tolerant, highly scalable GPU orchestration
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs). -
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Masuratori optimizate CMM
Sabloane masuratori
Aceste sabloane sunt create pentru a putea oferi o gama larga de programe create de la zero pentru corpuri cu forme complexe din viata de zi cu zi ce vor servi ca ajutor de baza utilizatorilor de CMM-uri din intreaga lume. -
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Introduction to ML with Python
Notebooks and code for the book "Introduction to Machine Learning
Introduction to Machine Learning with Python is the companion code repository for the book by Andreas Mueller and Sarah Guido. It provides the Jupyter notebooks used to develop the book, allowing readers to reproduce examples and visualizations. Chapters cover machine-learning foundations, supervised and unsupervised methods, feature engineering, model evaluation, pipelines, and text processing. -
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torchtext
Data loaders and abstractions for text and NLP
We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C++... -
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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 detection, and classification. ... -
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Tensor Puzzles
Solve puzzles. Improve your pytorch
Tensor Puzzles is an interactive collection of 21 exercises for learning tensor programming in PyTorch and NumPy. Each puzzle asks the learner to recreate a familiar array operation from first principles. Solutions must fit on one short line and use only a restricted set of indexing, arithmetic, comparison, and broadcasting tools. Standard convenience functions such as sum, view, squeeze, and take are intentionally prohibited. -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
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Deep Learning Models
A collection of various deep learning architectures, models, and tips
This repository collects clear, well-documented implementations of deep learning models and training utilities written by Sebastian Raschka. The code favors readability and pedagogy: components are organized so you can trace data flow through layers, losses, optimizers, and evaluation. Examples span fundamental architectures—MLPs, CNNs, RNN/Transformers—and practical tasks like image classification or text modeling. -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws on... -
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snntorch
Deep and online learning with spiking neural networks in Python
snntorch is a deep learning library that enables researchers and developers to build and train spiking neural networks using the PyTorch framework. Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. ...