Concepts to understand

Machine Learning Coursera's by Andrew Ng
Mean squared error
Normalization, Rescaling
Cost function
Activation functions (tanh, sigmoid, etc.)
Gradient descent
Backpropagation
Optimizer function (overfitting, regularization/penalizing)

Gatys Image Style Transfer paper
Content loss function
Style loss function
Gram Matrix

• Denoising loss function

• Convolutional Neural Network

  1. Filtering
  2. Convolution layer
  3. Pooling layer
  4. Rectified Linear Units layer
  5. Fully connected layer

Papers

Image Style Transfer Using Convolutional Neural Networks

• http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf

Preserving Color in Neural Artistic Style Transfer

• https://arxiv.org/pdf/1606.05897.pdf

Courses

Coursera's Machine Learning by Andrew Ng

• https://www.coursera.org/learn/machine-learning

CS231n Convolutional Neural Networks for Visual Recognition

• http://cs231n.github.io/
• CS231n Winter 2016 Stanford Playlist

Coursera's Neural Networks by Geoffrey Hinton

• https://www.coursera.org/learn/neural-networks

Other resources

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

• http://www.deeplearningbook.org/

MOOCs, books, etc.

• https://www.reddit.com/r/MachineLearning/wiki/index

Tensorflow resources

• https://github.com/jtoy/awesome-tensorflow

tensorflow 1.0.0
numpy 1.12.1
python 3.x

(may be used)
opencv 3.2.0
scipy 0.18.1
pillow 4.0.0

cuda/cudnn (if running on GPU)