#cplusplus #cuda #deep_learning #deep_neural_networks #distributed #machine_learning #ml #neural_network

https://github.com/Oneflow-Inc/oneflow

#cplusplus #cuda #deep_learning #gpu #mlp #nerf #neural_network #real_time #rendering

https://github.com/NVlabs/tiny-cuda-nn

#cuda #3d_reconstruction #computer_graphics #computer_vision #function_approximation #machine_learning #nerf #neural_network #real_time #real_time_rendering #realtime #signed_distance_functions

https://github.com/NVlabs/instant-ngp

#python #anomaly_detection #anomaly_localization #anomaly_segmentation #neural_network_compression #openvino #unsupervised_learning

https://github.com/openvinotoolkit/anomalib

#python #action_recognition #anomaly_detection #audio_processing #background_removal #crowd_counting #deep_learning #face_detection #face_recognition #fashion_ai #gan #hand_detection #image_classification #image_segmentation #machine_learning #neural_network #object_detection #object_recognition #object_tracking #pose_estimation

https://github.com/axinc-ai/ailia-models

#jupyter_notebook #andrew_ng #andrew_ng_course #andrew_ng_machine_learning #andrewng #coursera #coursera_machine_learning #data_science #deep_learning #deep_neural_networks #dl #machine_learning #ml #neural_network #neural_networks #numpy #pandas #python #pytorch #reinforcement_learning

https://github.com/ashishpatel26/Andrew-NG-Notes

#other #ai #artificial_intelligence #guide #keywords #midjourney #neural_network #reference #styles

https://github.com/willwulfken/MidJourney-Styles-and-Keywords-Reference

#python #anime4k #machine_learning #ncnn #neural_network #qt5 #realsr #rife #srmd #super_resolution #upscaling #video #video_enlarger #vulkan #waifu2x

https://github.com/k4yt3x/video2x

#other #2022 #ai #artificial_intelligence #computer_science #computer_vision #deep_learning #innovation #machine_learning #machinelearning #neural_network #paper #papers #python #sota #state_of_art #state_of_the_art #technology

https://github.com/louisfb01/best_AI_papers_2022

#python #cifar10 #david_page #deep_learning #experimentation #language_models_are_next #machine_learning #neural_network #resnet9 #responsive_to_issue_tickets #world_record

https://github.com/tysam-code/hlb-CIFAR10

#rust #approximate_nearest_neighbor_search #embeddings_similarity #hnsw #image_search #knn_algorithm #machine_learning #matching #mlops #nearest_neighbor_search #neural_network #neural_search #recommender_system #search #search_engine #search_engines #similarity_search #vector_database #vector_search #vector_search_engine

https://github.com/qdrant/qdrant

#jupyter_notebook #computer_vision #deep_learning #image_classification #imagenet #neural_network #object_detection #pretrained_models #pretrained_weights #pytorch #semantic_segmentation #transfer_learning

https://github.com/Deci-AI/super-gradients

#python #dataset #deep_learning #machine_learning #neural_network #pytorch #streaming

https://github.com/mosaicml/streaming

#rust #deep_learning #machine_learning #neural_network #pytorch

https://github.com/LaurentMazare/tch-rs

#python #autograd #deep_learning #gpu #machine_learning #neural_network #numpy #python #tensor

PyTorch is a powerful Python package that helps you with tensor computations and deep neural networks. It uses strong GPU acceleration, making your computations much faster. Here are the key benefits PyTorch allows you to use GPUs for tensor computations, similar to NumPy, but much faster.
- **Flexible Neural Networks** You can seamlessly use other Python packages like NumPy, SciPy, and Cython with PyTorch.
- **Fast and Efficient**: PyTorch has minimal framework overhead and is highly optimized for speed and memory efficiency.

Overall, PyTorch makes it easier and faster to work with deep learning projects by providing a flexible and efficient environment.

https://github.com/pytorch/pytorch

#cplusplus #deep_learning #deep_neural_networks #distributed #machine_learning #ml #neural_network #python #tensorflow

TensorFlow is a powerful tool for machine learning that helps you build and deploy AI applications easily. It was developed by Google and is now open source, meaning anyone can use and contribute to it. TensorFlow provides tools, libraries, and a strong community to support your work. You can install it using Python with a simple command like `pip install tensorflow`, and it supports various devices including GPUs. This makes it versatile for researchers and developers alike, allowing you to push the boundaries of machine learning and create innovative applications.

https://github.com/tensorflow/tensorflow

#c_lang #convolutional_neural_network #convolutional_neural_networks #cpu #inference #inference_optimization #matrix_multiplication #mobile_inference #multithreading #neural_network #neural_networks #simd

XNNPACK is a powerful tool that helps make neural networks run faster on various devices like smartphones, computers, and Raspberry Pi boards. It supports many different types of processors and operating systems, making it very versatile. XNNPACK doesn't work directly with users but instead helps other machine learning frameworks like TensorFlow Lite, PyTorch, and ONNX Runtime to perform better. This means your apps and programs that use these frameworks can run neural networks more quickly and efficiently, which is beneficial because it saves time and improves performance.

https://github.com/google/XNNPACK

#python #ai #artificial_intelligence #cython #data_science #deep_learning #entity_linking #machine_learning #named_entity_recognition #natural_language_processing #neural_network #neural_networks #nlp #nlp_library #python #spacy #text_classification #tokenization

spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.

https://github.com/explosion/spaCy

#python #deep_learning #intel #machine_learning #neural_network #pytorch #quantization

Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models.

https://github.com/intel/intel-extension-for-pytorch

#cplusplus #arm #baidu #deep_learning #embedded #fpga #mali #mdl #mobile #mobile_deep_learning #neural_network

Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance.

https://github.com/PaddlePaddle/Paddle-Lite