Sequential regulatory activity predictions with deep convolutional neural networks.

An Open Source Machine Learning Framework for Everyone

Sequence-to-sequence model with LSTM encoder/decoders and attention

We use a modified neural network instead of Gaussian process for Bayesian optimization.

A Python implementation of global optimization with gaussian processes.

Autograd automatically differentiates native Torch code

Convolutional neural network analysis for predicting DNA sequence activity.

Use Spearmint via Torch.

Automatic Caffe parameter search via Spearmint Bayesian optimisation

A fault tolerant, protocol-agnostic RPC system

Torch installation in a self-contained folder

Code for Kaggle-CIFAR10 competition. 5th place.

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

Efficiently computes derivatives of NumPy code.

A GPU implementation of Convolutional Neural Nets in C++

Spearmint Bayesian optimization codebase

Kayak is a library for automatic differentiation with applications to deep neural networks.

GPU-Accelerated Deep Learning Library in Python

Given a web API, Generate client libraries in node, php, python, ruby

Implementation of some deep learning algorithms.

A summary of parallelizing moderate amounts of work in Python

StarCluster is an open source cluster-computing toolkit for Amazon's Elastic Compute Cloud (EC2).

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Laroche…

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Laroche…