Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.
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A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Low-code framework for building custom LLMs, neural networks, and other AI models
A paper list of object detection using deep learning.
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A collection of libraries to optimise AI model performances
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Effortless data labeling with AI support from Segment Anything and other awesome models.
A general-purpose encoder-decoder framework for Tensorflow
Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
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A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks
Flops counter for neural networks in pytorch framework
Learning Chinese Character style with conditional GAN
Deep neural network to extract intelligent information from invoice documents.
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)