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A curated list of awesome AI, ML, and DL resources, including code, tutorials, and study notes.
A tutorial for deep learning researchers to understand the basics of distribution probability.
A Python framework for quick implementation of recommender systems using TensorFlow.
A Python library for synthetic data curation and structured data extraction for machine learning models.
A standard framework for modeling deep learning models for tabular data in PyTorch.
Hyperparameter optimization library for TensorFlow and Keras, focused on AI/ML model development.
A collection of notes on deep learning by Li Hong-mei, a renowned expert in the field.
DLRover is an automatic distributed deep learning system for training large-scale AI models on Kubernetes.
Grad-CAM is a deep learning visualization technique for interpreting and explaining CNN-based models.
A collection of Python code and resources related to natural language processing tasks like topic modeling, text classification, and machine translation.
Research interface for blackbox and hyperparameter optimization using the Google Vizier Service
A roadmap to become a Visual SLAM (Simultaneous Localization and Mapping) developer in 2023.
A comprehensive survey of deep learning techniques for 3D point cloud analysis, covering classification, detection, segmentation, and tracking.
A guide to learn about Transformer models, a foundational architecture for natural language processing.
A deep learning gateway for Raspberry Pi and other edge devices, enabling AI inference at the edge.
A library focused on deep-diving into Go concurrency programming concepts and techniques.
Open-source tools for computational pathology and digital pathology research using deep learning and weakly-supervised learning.
A Jupyter Notebook-based course on accelerated computer vision using AWS Machine Learning University.
PaddleSlim is an open-source library for deep model compression and architecture search.
Fast AutoAugment is an official PyTorch implementation for automatic data augmentation.
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