#python #deep_neural_networks #deployment #detection #neural_networks #classification #segmentation #resnet #deeplearning #unet #industry #jetson #mobilenet #yolov3

https://github.com/PaddlePaddle/PaddleX

#python #deep_learning #forecasting #machine_learning #mxnet #neural_networks #pytorch #time_series #time_series_forecasting #time_series_prediction

https://github.com/awslabs/gluon-ts

#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

#python #deep_learning #machine_learning #ml_efficiency #ml_systems #ml_training #neural_networks #pytorch

https://github.com/mosaicml/composer

#python #anomaly_detection #benchmark #data_mining #data_sicence #deep_learning #ensemble_learning #machine_learning #neural_networks #outlier_detection #semi_supervised_learning #supervised_learning #unsupervised_learning

https://github.com/Minqi824/ADBench

#other #awesome_list #deep_learning #differential_geometry #group_theory #neural_computation #neural_networks #neuroscience #papers #toplogical_data_analysis

https://github.com/neurreps/awesome-neural-geometry

#python #bloom #deep_learning #distributed_systems #language_models #large_language_models #machine_learning #neural_networks #pytorch #volunteer_computing

https://github.com/bigscience-workshop/petals

#cplusplus #ai_framework #deep_learning #hardware_acceleration #machine_learning #neural_networks #onnx #pytorch #scikit_learn #tensorflow

ONNX Runtime is a tool that makes machine learning faster and cheaper. It works on many different devices and operating systems, like Windows, Linux, and Mac, and supports popular machine learning frameworks like PyTorch and TensorFlow. This means you can use it to speed up your machine learning models, making your applications run faster and more efficiently. It also helps in training models quickly, especially on powerful NVIDIA GPUs. This benefits you by providing faster customer experiences and lower costs for your machine learning projects.

https://github.com/microsoft/onnxruntime

#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 #asr #deeplearning #generative_ai #large_language_models #machine_translation #multimodal #neural_networks #speaker_diariazation #speaker_recognition #speech_synthesis #speech_translation #tts

NVIDIA NeMo is a powerful, easy-to-use platform for building, customizing, and deploying generative AI models like large language models (LLMs), vision language models, and speech AI. It lets you quickly train and fine-tune models using pre-built code and checkpoints, supports the latest model architectures, and works on cloud, data center, or edge environments. NeMo 2.0 is even more flexible and scalable, with Python-based configuration and modular design, making it simple to experiment and scale up. The main benefit is that you can create advanced AI applications faster, with less effort, and at lower cost, while getting high performance and easy deployment options[1][2][3].

https://github.com/NVIDIA/NeMo