Mastering Neural Style Transfer: Tensorflow, Keras & Python

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Karthik K

1:22:25

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1 - Fundamentals

1 - Introduction.mp4

02:07

2 - What is Neural Style Transfer.mp4

02:35

3 - About this Project.mp4

02:14

4 - Why Should we Learn.mp4

03:42

5 - Applications.mp4

04:15

6 - Why Keras and Python.mp4

02:37

7 - Why Google Colab.mp4

02:54

2 - Model Building and Prediction

8 - Setup the Working Directory.mp4

01:30

8 - code.zip

8 - content-image.zip

8 - style-image.zip

9 - Contents in Directory.mp4

02:24

10 - Activate GPU.mp4

01:46

11 - Checking the availability and usage of GPUs.mp4

03:00

12 - Mount Google Drive to Google Colab.mp4

02:19

13 - Necessary library imports.mp4

02:55

14 - Setting the directory path.mp4

02:53

15 - Displaying the base image and the style reference.mp4

02:09

16 - Defining the desired dimensions.mp4

02:17

17 - Preprocesses an image.mp4

02:55

18 - Convert the generated image back to its original format.mp4

03:11

19 - Calculate the Gram matrix.mp4

03:08

20 - Calculates the style loss.mp4

02:25

21 - Calculates the content loss.mp4

01:30

22 - Calculates the total variation loss.mp4

01:49

23 - Loading the VGG19.mp4

02:13

24 - Creating a dictionary.mp4

01:50

25 - Building a feature extraction model.mp4

02:33

26 - Define the names of the style layers and the content layer.mp4

02:21

27 - Set the weights.mp4

02:17

28 - Calculates the total loss.mp4

03:27

29 - Computes the loss and gradients.mp4

02:02

30 - Set up the optimizer.mp4

02:06

31 - Preprocess the base image style reference image and combination image.mp4

02:35

32 - Perform the style transfer optimization loop.mp4

02:28

33 - Save and display the final generated image.mp4

01:58

Description

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

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.

Who is this 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

What You Need to Know?

Familiarity with Python programming language (basic knowledge is sufficient)

More details

Description
Welcome to the exciting world of Neural Style Transfer! In this comprehensive course, you will embark on a journey to master the art of transforming ordinary images into captivating artworks using cutting-edge techniques. Harness the power of Google Colab, Tensorflow, Keras, and Python to unlock your creativity and unleash the potential of Neural Style Transfer.
Throughout this course, you will delve deep into the fascinating realm of artistic image generation. From understanding the fundamentals of Neural Style Transfer to exploring advanced generative adversarial networks, you will gain the knowledge and skills needed to create stunning visual masterpieces.
Guided by industry experts, you will learn to leverage the power of Google Colab's cloud computing capabilities to seamlessly execute resource-intensive tasks, allowing you to focus on unleashing your creativity without worrying about hardware limitations.
By the end of this course, you will not only possess a deep understanding of Neural Style Transfer and its practical implementation, but you will also have a captivating portfolio of artistic images to showcase your skills. With the demand for AI-driven image manipulation on the rise, this course will equip you with the expertise sought after by employers across various industries.
Prepare to step into a world of endless creative possibilities and embark on a rewarding career journey. Enroll now and unlock the door to exciting job opportunities in fields such as graphic design, advertising, entertainment, and more. Let your artistic vision take flight as you become a master of Neural Style Transfer!
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

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Engineer dedicated to utilizing the power of Machine learning and Deep learning to solve real-world problems, improve design and performance assessment. Over ten years of experience in engineering and R&D environment. Engineering professional with a focus on Multi-physics CFD-ML from IIT Madras. Experienced in implementing action-oriented solutions to complex business problem.

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