Displaying 1 to 20 from 26 results

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

  •    Jupyter

This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

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.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

zhihu - This repo contains the source code in my personal column (https://zhuanlan

  •    Jupyter

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

TensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

  •    Python

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

  •    Python

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

  •    Python

Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

Autoencoders - Torch implementations of various types of autoencoders

  •    Lua

Different models can be chosen using th main.lua -model <modelName>. The denoising criterion can be used to replace the standard (autoencoder) reconstruction criterion by using the denoising flag. For example, a denoising AAE (DAAE) [10] can be set up using th main.lua -model AAE -denoising. The corruption process is additive Gaussian noise *~ N(0, 0.5)*.

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

  •    Jupyter

Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

vae-style-transfer - An experiment in VAE-based artistic style transfer by embedding fiddling.

  •    Python

The project was created as part of the Creative Applications of Deep Learning with TensorFlow (CADL) Kadenze course's final assignment. It is an experimental attempt to transfer artistic style learned from a series of paintings "live" onto a video sequence by fitting a variational autoencoder with 512 codes to both paintings and video frames, isolating the mean feature-space embeddings and modifying the video's embeddings to be closer to those of the paintings. Because the general visual quality of the VAE's decoded output is relatively low, a convolutional post-processing network based on residual convolutions was trained with the purpose of making the resulting image less similar to the VAE's generated output and more similar to the original input images. The basic idea was to have an upsampling network here, but it quickly turned out to be a very naive idea at this point of development. Instead, it now downsizes the input, learns filters in a residual network and then samples back up to the input frame size; I would have liked to perform convolutions directly on the input, but memory limitations prevented the usage of a useful amount of feature maps.

vae-pytorch - AE and VAE Playground in PyTorch

  •    Jupyter

Disclaimer: VAE coming soon... The last activation of the decoder layer, the loss function, and the normalization scheme used on the training data are crucial for obtaining good reconstructions and preventing exploding negative losses.

tensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms

  •    Jupyter

Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

splitbrainauto - Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

  •    Shell

This repository contains a pre-trained Split-Brain Autoencoder network. The network achieves state-of-the-art results on several large-scale unsupervised representation learning benchmarks. This code requires a working installation of Caffe. For guidelines and help with installation of Caffe, consult the installation guide and Caffe users group.

Unsupervised-Classification-with-Autoencoder - Using Autoencoders for classification as unsupervised machine learning algorithms with Deep Learning

  •    Jupyter

Using Autoencoders for classification as unsupervised machine learning algorithms with Deep Learning. Project in Unsupervised Classification With Autoencoder.ipynb file.

pointnet-autoencoder - Autoencoder for Point Clouds

  •    Python

This repository is under the MIT license. See the LICENSE file for detail. We need TensorFlow (version>=1.4).

sldm4-h2o - Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

  •    HTML

This repository contains the H2O presentation for Trevor Hastie and Rob Tibshirani's Statistical Learning and Data Mining IV course in Washington, DC on October 19, 2016.

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