Displaying 1 to 8 from 8 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.

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.

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.

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.

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.