Mastering Neural Style Transfer: Tensorflow, Keras & Python

Name: Mastering Neural Style Transfer: Tensorflow, Keras & Python
Rating: 4.4 (10 reviews)

Hands-on Neural Style Transfer: Creating Artistic Images using Tensorflow, Keras, Python, and Google Colab

Created byKarthik Karunakaran, Ph.D.

Last updated 7/2023

English

What you'll learn

Understand Neural Style Transfer and its application in combining content and style in images.
Learn to implement Neural Style Transfer algorithms using Python and Keras.
Gain proficiency in image preprocessing techniques and using pre-trained models like VGG19.
Understand the concept of loss functions and their role in style transfer optimization.
Acquire skills in optimizing style transfer using an optimizer with learning rate decay.
Learn to save and display generated images during the optimization process.
Gain practical experience in implementing Neural Style Transfer algorithms.

Course content

2 sections • 33 lectures • 1h 22m total length

Introduction2:07
What is Neural Style Transfer?2:35
About this Project2:14
Why Should we Learn?3:42
Applications4:15
Why Keras and Python?2:37
Why Google Colab?2:54

Setup the Working Directory1:30
Contents in Directory2:24
Review the files in the style transfer folder, including content image jpg, style image jpg, and code ipynb, to see how content and style guide the transfer.
Activate GPU1:46
Checking the availability and usage of GPUs3:00
Mount Google Drive to Google Colab2:19
Necessary library imports2:55
Learn to import essential libraries for neural style transfer with TensorFlow and Keras, including NumPy, OS, Vgg 19, IPython display, and PIL for array operations and image handling.
Setting the directory path2:53
Set the directory path for style transfer images and define the base image and style reference image paths with os.path.join. Run the code cell and ensure the files exist.
Displaying the base image and the style reference2:09
Defining the desired dimensions2:17
Preprocesses an image2:55
Convert the generated image back to its original format3:11
Calculate the Gram matrix3:08
Calculates the style loss2:25
Calculates the content loss1:30
Calculates the total variation loss1:49
Loading the VGG192:13
Creating a dictionary1:50
Building a feature extraction model2:33
Define the names of the style layers and the content layer2:21
Set the weights2:17
Calculates the total loss3:27
Computes the loss and gradients2:02
Set up the optimizer2:06
Set up stochastic gradient descent optimizer in TensorFlow Keras with an exponential decay learning rate starting at 100, decaying by 0.96 per step to improve convergence in neural style transfer.
Preprocess the base image, style reference image, and combination image2:35
Preprocess the base image, style reference image, and combination image by loading, resizing, converting to arrays, expanding dims, applying Vgg 19 preprocessing, and converting to tensors, initializing the combination image.
Perform the style transfer optimization loop2:28
Save and display the final generated image1:58
Save and display the final generated image after a neural style transfer run, loading iteration 4000 PNG, displaying it in the notebook, and saving the result with PIL.

Requirements

Familiarity with Python programming language (basic knowledge is sufficient)

Description

Step into the captivating world of Neural Style Transfer, where ordinary images are transformed into mesmerizing works of art using cutting-edge techniques. Get ready for an immersive journey that will empower you to unleash your creativity and unlock the full potential of this revolutionary technology, guided by experts in the field.

Throughout this comprehensive course, you will explore the fascinating realm of artistic image generation, starting with the fundamentals of Neural Style Transfer and advancing to delve into the realm of advanced generative adversarial networks. Armed with the powerful trio of Google Colab, TensorFlow, and Keras, you'll have all the tools you need to bring your artistic visions to life.

Unleash your imagination without the constraints of hardware limitations, as you harness the unparalleled computing capabilities of Google Colab's cloud platform. This means you can focus wholeheartedly on refining your artistry and honing your skills, leaving the technical worries behind.

By the course's conclusion, you will not only possess a profound understanding of Neural Style Transfer and its practical implementation, but you'll also have a captivating portfolio of awe-inspiring images that showcase your newfound talent. As the demand for AI-driven image manipulation skyrockets, this course will provide you with the expertise that employers across various industries actively seek.

Embrace a world of boundless creative possibilities and set forth on a rewarding career journey. Enroll now to unlock the gateway to thrilling job opportunities in graphic design, advertising, entertainment, and beyond. Allow your artistic vision to soar as you master the art of Neural Style Transfer, creating captivating visuals that will leave a lasting impression on the world!

Who this course is for:

Beginners interested in deep learning and computer vision
Students studying computer science, artificial intelligence, or related fields
Professionals looking to enhance their skills in neural style transfer and generative adversarial networks
Developers interested in learning how to implement image processing techniques using Python and Keras
Individuals with a curiosity for creative applications of artificial intelligence in the field of image generation and style transfer

Mastering Neural Style Transfer: Tensorflow, Keras & Python

What you'll learn

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Course content

Fundamentals7 lectures • 20min

Model Building and Prediction26 lectures • 1hr 2min

Requirements

Description

Who this course is for: