Deep Learning
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An object-oriented machine learning framework built on JAX, designed for simplicity and readability in research.
A vision transformer foundation model pre-trained on over 200 million pathology images for computational pathology tasks.
A curated list of resources for Question Answering (QA), covering machine learning, deep learning, datasets, and research.
A lightweight C library for building and training small to medium artificial neural networks with minimal dependencies.
JAX (Flax) implementations of reinforcement learning algorithms for continuous action spaces, designed for research.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A benchmark for evaluating protein language models through five biologically relevant semi-supervised learning tasks.
A PyTorch-based deep learning model for simultaneous nuclear instance segmentation and classification in histopathology images.
NeuPy is a Tensorflow based python library for prototyping and building neural networks
ROS package implementing a deep reinforcement learning algorithm for dynamic obstacle avoidance in ground robots.
Interactive segmentation and tracking tools for microscopy images built on Segment Anything.
A curated collection of Monte Carlo tree search research papers with implementations from top AI conferences.
A fast and flexible deep learning system with NumPy-like NDarray interface and easy multi-GPU support.
A deep learning library built on Chainer for molecular property prediction using graph convolutional neural networks.
TensorFlow port for AMD GPUs via ROCm, enabling machine learning on Radeon hardware.
Generates realistic handwriting using LSTM Mixture Density Networks implemented in TensorFlow.
Real-time object detection on Android using YOLO with TensorFlow, detecting 20 object classes from the Pascal VOC dataset.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A deep learning library for tag estimation and semantic feature vector extraction from illustrations.
A PyTorch adaptation of Lucid for visualizing and interpreting neural networks through feature visualization.
A curated list of awesome Torch tutorials, projects, libraries, and communities for deep learning.
A lightweight, portable pure C99 ONNX inference engine for embedded devices with hardware acceleration support.
A free Google Colab-based toolbox with Jupyter notebooks and GUI for applying deep learning to microscopy data without coding expertise.
A JAX-native library of probability distributions and bijectors, reimplementing a subset of TensorFlow Probability with emphasis on readability and extensibility.
A curated list of deep learning resources for video-text retrieval, including papers, implementations, and datasets.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
Build fully-functioning computer vision and object detection models with PyTorch in just 5 lines of code.
High-resolution de novo protein structure prediction from amino acid sequences using deep learning.
A JAX library for nonlinear optimization including root finding, minimization, fixed points, and least squares.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.
Classify music genre from a 10-second audio stream using a convolutional neural network trained on mel-frequency spectrograms.
A convolutional network-based image classifier and feature extractor trained on ImageNet, providing dense feature extraction capabilities.
TensorFlow implementation of GAN-CLS algorithm for generating images from text descriptions using adversarial networks.
A deep learning system that classifies food images into 230 categories and retrieves matching recipes using convolutional neural networks.
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