Deep Learning
Showing 36 of 402 projects
Generate comprehensive data quality profiles and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
A curated collection of must-use resources for AI engineering, including books, courses, papers, frameworks, and tools.
A curated list of must-use resources for AI engineering, including books, courses, papers, frameworks, and tools.
Google's IPython Notebook demonstrating neural network art generation using the DeepDream technique.
A reliable PyTorch implementation of reinforcement learning algorithms for research and industry.
NVIDIA's SDK for high-performance deep learning inference optimization and deployment on NVIDIA GPUs.
A PyTorch library providing state-of-the-art methods for generating visual explanations (Class Activation Maps) for computer vision models.
A drop-in replacement for the MNIST dataset, featuring 70,000 Zalando fashion article images for benchmarking machine learning algorithms.
Official JAX/Flax implementation of Vision Transformer (ViT) and MLP-Mixer for image recognition, with pre-trained models.
A comprehensive study plan and resource collection for preparing for machine learning engineering interviews at top tech companies.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring hands-on tutorials, courses, and ethical considerations.
An open-source LLMOps platform unifying gateway, observability, evaluation, optimization, and experimentation for industrial-grade LLM applications.
A Python library for language-vision intelligence research, providing unified access to state-of-the-art models, datasets, and tasks.
A differentiable computer vision library for PyTorch, providing geometric vision and image processing algorithms for AI workflows.
A Python library for building custom machine learning models for tasks like image classification, object detection, and recommendations.
A curated list of deep learning resources for computer vision, including papers, courses, books, and software.
A curated list of deep learning resources for computer vision, including papers, courses, books, and software.
A research framework for fast prototyping of reinforcement learning algorithms, designed for easy experimentation and reproducibility.
A curated list of semantic segmentation papers, code, datasets, and resources across various deep learning frameworks.
An open-source inference serving platform for deploying AI models from multiple frameworks across cloud, data center, and edge devices.
An elegant PyTorch-based deep reinforcement learning library with modular APIs for both research and application development.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
Code examples and tutorials for Stanford's TensorFlow for Deep Learning Research course (CS 20).
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A curated repository of resources, datasets, and research papers for 3D machine learning, covering computer vision, graphics, and deep learning.
A comprehensive resource of deep learning techniques and models for analyzing satellite and aerial imagery.
A TensorFlow 2 library providing simple, composable abstractions for machine learning research via the snt.Module concept.
A Python library for performing data science and machine learning on data without direct access, using remote datasites.
A PyTorch library providing efficient, reusable components for deep learning with 3D data, including mesh operations and differentiable rendering.
An end-to-end speech processing toolkit for speech recognition, text-to-speech, translation, enhancement, and more.
A deep reinforcement learning library offering high-quality, single-file implementations of algorithms like PPO, DQN, and SAC for research and education.
A modular deep learning library providing a higher-level API for TensorFlow to speed up experimentation.
A Python library for flexible and readable tensor operations across numpy, PyTorch, JAX, TensorFlow, and other frameworks.
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