Deep Learning
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A lightweight Python library for building reproducible machine learning pipelines with minimal interface constraints.
A community-driven collection of end-to-end tutorials for creating and deploying TensorFlow Lite models on mobile devices.
A machine learning algorithm for accurate, energy-efficient outdoor positioning using 5G mmWave beamformed fingerprints.
A symbolic programming library built on JAX for concise, explicit, and optimized machine learning computations.
A lightweight Python library for explicit, type-checked function configuration via a centralized Python file.
A tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
Tutorial materials for the 2012 IPAM Graduate Summer School on Deep Learning and Feature Learning using Theano and Torch.
A JVM library providing the lowest barrier of entry to image processing, computer vision, and neural networks using OpenCV.
A deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
A hands-on workshop introducing deep learning concepts with practical examples using neural networks, CNNs, RNNs, and autoencoders.
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
An experimental library that converts TensorFlow functions and graphs into JAX functions for reuse and fine-tuning within JAX codebases.
A TensorFlow implementation of the Mnemonic Descent Method for end-to-end face alignment.
A knowledge-informed cross-species foundation model pre-trained on over 120 million human and mouse single-cell transcriptomes to decipher universal gene regulatory mechanisms.
A PyTorch-based deep learning library for building and training spiking convolutional neural networks with hardware deployment support.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A freely usable dataset of over 5,000 labeled clothing images across 20 categories for machine learning projects.
A 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
A deep learning framework for predicting chromatin profiles and sequence regulatory activities from DNA sequences and variants.
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
A Haskell library for building and training feed-forward neural networks with automatic differentiation.
A deep learning model for classifying image aesthetic quality using Inception modules and fine-tuned connected layers.
A lightweight Clojure wrapper for TensorFlow's Java API, providing idiomatic access to machine learning operations.
A Python package providing popular computer vision model architectures built with Equinox for JAX.
A DSL-based library for unified tensor reshaping, squeezing, expanding, and transposing in JAX, TensorFlow, and NumPy.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A collection of Google Colab tutorials teaching biologists how to apply deep learning with Keras to real-world biological and agricultural problems.
A tutorial and demo using Hyperopt to auto-optimize CNN architecture and hyperparameters for the CIFAR-100 dataset with Keras/TensorFlow.
A Python toolbox for analyzing multiplexed imaging data, featuring segmentation, pixel/cell clustering, and spatial analysis.
A performant JAX reimplementation of the UniRep model for generating protein sequence representations.
A flexible deep learning framework for Ruby, ported from Python's Chainer.
A curated collection of readings and resources for relational deep learning models applied to drug pair scoring tasks.
A curated collection of resources and survey paper on relational deep learning methods for drug pair scoring tasks.
A Keras implementation of Ladder Networks for semi-supervised learning, achieving 98% accuracy on MNIST with only 100 labeled examples.
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