Month: January 2021

Fast Neural Style Transfer by PyTorch (Mac OS)

2021-Jan-31: The git repo has been upgraded from PyTorch 0.3.0 to PyTorch 1.7.0 with Python 3.8.3.

C. Cui's Blog

2021-Jan-31: The git repo has been upgraded from PyTorch 0.3.0 to PyTorch 1.7.0.

Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.

The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

More details about the algorithm could be found in the following papers:

  1. Perceptual Losses for Real-Time Style Transfer and Super-Resolution ;
  2. Instance Normalization: The Missing Ingredient for Fast Stylization.

If you could not download the papers, here are the Papers.

You can find all the source code and images at my GitHub: fast_neural_style .


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Deep Learning Specialization on Coursera

Introduction

This repo contains all my work for this specialization. The code and images, are taken from Deep Learning Specialization on Coursera.

In five courses, you are going learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

Continue reading “Deep Learning Specialization on Coursera”
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