aipnd-project - AIML Programming with PyTorch

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Image categories are found in cat_to_name.json and flower images can be downloaded in the gziped tar file flower_data.tar.gz from Udacity. You should now have test, train and valid directories containing classification directories and flower images under the flowers directory.



Related Projects

udacity-nanodegrees - :mortar_board: List of Udacity Nanodegree programs with links to the free courses in their curricula


Udacity offers some great content in their Nanodegree programs. If you don't have the money, and/or just don't want to pay for them, you can take many of the courses for free. While it's no substitute for the actual Nanodegree programs (which include project reviews, additional student and career guidance, and a job guarantee) there is some great content available for learning.

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

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This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

OpenNMT-py - Open Source Neural Machine Translation in PyTorch

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This is a Pytorch port of OpenNMT, an open-source (MIT) neural machine translation system. It is designed to be research friendly to try out new ideas in translation, summary, image-to-text, morphology, and many other domains. Codebase is relatively stable, but PyTorch is still evolving. We currently only support PyTorch 0.4 and recommend forking if you need to have stable code.

Deep-Learning-Boot-Camp - A community run, 5-day PyTorch Deep Learning Bootcamp

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Tel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

fairseq-py - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

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This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation. Currently fairseq-py requires PyTorch version >= 0.3.0. Please follow the instructions here:

deep-learning - Repo for the Deep Learning Nanodegree Foundations program.

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This repository contains material related to Udacity's Deep Learning Nanodegree Foundation program. It consists of a bunch of tutorial notebooks for various deep learning topics. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. There are other topics covered such as weight intialization and batch normalization. There are also notebooks used as projects for the Nanodegree program. In the program itself, the projects are reviewed by Udacity experts, but they are available here as well.

PyTorch-NLP - Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

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PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

PyTorch-Tutorial - Build your neural network easy and fast

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In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

tch-rs - Rust bindings for PyTorch

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Rust bindings for PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorch). It aims at staying as close as possible to the original C++ api. More idiomatic rust bindings could then be developed on top of this. The documentation can be found on The code generation part for the C api on top of libtorch comes from ocaml-torch.

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

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Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

T2F - T2F: text to face generation using Deep Learning

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Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

spotlight - Deep recommender models using PyTorch.

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pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

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This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

translate - Translate - a PyTorch Language Library

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Translate is a library for machine translation written in PyTorch. It provides training for sequence-to-sequence models. Translate relies on fairseq, a general sequence-to-sequence library, which means that models implemented in both Translate and Fairseq can be trained. Translate also provides the ability to export some models to Caffe2 graphs via ONNX and to load and run these models from C++ for production purposes. Currently, we export components (encoder, decoder) to Caffe2 separately and beam search is implemented in C++. In the near future, we will be able to export the beam search as well. We also plan to add export support to more models. Provided you have CUDA installed you should be good to go.

Seq2Seq-PyTorch - Sequence to Sequence Models with PyTorch

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A vanilla sequence to sequence model presented in, consits of using a recurrent neural network such as an LSTM ( or GRU ( to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in

Pyro - Deep universal probabilistic programming with Python and PyTorch

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Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

ignite - High-level library to help with training neural networks in PyTorch

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Ignite is a high-level library to help with training neural networks in PyTorch. As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when "rolling your own" training loop.

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