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.
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 tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
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 TensorFlow implementation of the Mnemonic Descent Method for end-to-end face alignment.
An experimental library that converts TensorFlow functions and graphs into JAX functions for reuse and fine-tuning within JAX codebases.
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 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.
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 belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A deep learning framework for predicting chromatin profiles and sequence regulatory activities from DNA sequences and variants.
A deep learning model for classifying image aesthetic quality using Inception modules and fine-tuned connected layers.
A Haskell library for building and training feed-forward neural networks with automatic differentiation.
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 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 collection of Google Colab tutorials teaching biologists how to apply deep learning with Keras to real-world biological and agricultural problems.
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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