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top_9_feature_engineering_techniques

toploader

A Python Interface for Julia Pkg environments.

topological_sort

Topological sort algorithm

topologies

Various topologies with Intel optimizations

topology-decision-boundaries

Topology of decision boundaries

topt

toqito

|toqito> (Theory of Quantum Information Toolkit) in Python :snake:

torch-autograd

Autograd automatically differentiates native Torch code

torch-distributions

Probability distributions, wrapped for Torch.

torch-hdf5

Torch interface to HDF5 library

torch-points3d

Pytorch framework for doing deep learning on point clouds.

torch-randomkit

Provides and wraps the Randomkit library, copied from Numpy.

torch-rnn

Efficient, reusable RNNs and LSTMs for torch

torch-tensorrt

PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT

torch-tripletembedding

TripletLoss used in Google's FaceNet paper

torch-video-tutorials

Light your way in Deep Learning with Torch 🔦

torch.jl

Sensible extensions for exposing torch in Julia.

torch.net

.NET bindings for PyTorch. Machine Learning with C# / F# with Multi-GPU/CPU support

torch.rb

Deep learning for Ruby, powered by LibTorch

torch7

http://torch.ch

torchbearer

torchbearer: A model fitting library for PyTorch

torchbeast

A PyTorch Platform for Distributed RL

torchbench

Easily benchmark machine learning models in PyTorch

torchcde

Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

torchcraft

Connecting Torch to StarCraft

torchcraftai

A platform that lets you build agents to learn to play StarCraft: Brood War.

torchcubicspline

Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, and backpropagation.

torchcv

A PyTorch-Based Framework for Deep Learning in Computer Vision

torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.