Deep Learning
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A curated list of must-use resources for AI engineering, including books, courses, papers, frameworks, and tools.
Generate comprehensive data quality profiles and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
Generate comprehensive data quality profiling and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
A reliable PyTorch implementation of reinforcement learning algorithms for research and industry.
Google's IPython Notebook demonstrating neural network art generation using the DeepDream technique.
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.
A comprehensive study plan and resource collection for preparing for machine learning engineering interviews at top tech companies.
Official JAX/Flax implementation of Vision Transformer (ViT) and MLP-Mixer for image recognition, with pre-trained models.
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.
An open-source LLMOps platform unifying gateway, observability, evaluation, optimization, and experimentation for industrial-grade LLM applications.
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring hands-on tutorials, courses, and ethical considerations.
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 elegant PyTorch-based deep reinforcement learning library with modular APIs for both research and application development.
An open-source inference serving platform for deploying AI models from multiple frameworks across cloud, data center, and edge devices.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
Code examples and tutorials for Stanford's TensorFlow for Deep Learning Research course (CS 20).
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 deep reinforcement learning library offering high-quality, single-file implementations of algorithms like PPO, DQN, and SAC for research and education.
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 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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