| 1 - Introduction.mp4 | 20.1 MB | ||
| 10 - Activate GPU.mp4 | 8.3 MB | ||
| 11 - Checking the availability and usage of GPUs.mp4 | 11.4 MB | ||
| 12 - Mount Google Drive to Google Colab.mp4 | 5.6 MB | ||
| 13 - Necessary library imports.mp4 | 8.8 MB | ||
| 14 - Setting the directory path.mp4 | 11.2 MB | ||
| 15 - Displaying the base image and the style reference.mp4 | 6.7 MB | ||
| 16 - Defining the desired dimensions.mp4 | 5.6 MB | ||
| 17 - Preprocesses an image.mp4 | 9 MB | ||
| 18 - Convert the generated image back to its original format.mp4 | 9.7 MB | ||
| 19 - Calculate the Gram matrix.mp4 | 8.6 MB | ||
| 2 - What is Neural Style Transfer.mp4 | 20.4 MB | ||
| 20 - Calculates the style loss.mp4 | 7.6 MB | ||
| 21 - Calculates the content loss.mp4 | 3.7 MB | ||
| 22 - Calculates the total variation loss.mp4 | 5.5 MB | ||
| 23 - Loading the VGG19.mp4 | 6.5 MB | ||
| 24 - Creating a dictionary.mp4 | 4.2 MB | ||
| 25 - Building a feature extraction model.mp4 | 6 MB | ||
| 26 - Define the names of the style layers and the content layer.mp4 | 6.6 MB | ||
| 27 - Set the weights.mp4 | 4.7 MB | ||
| 28 - Calculates the total loss.mp4 | 25.3 MB | ||
| 29 - Computes the loss and gradients.mp4 | 7.1 MB | ||
| 3 - About this Project.mp4 | 15.1 MB | ||
| 30 - Set up the optimizer.mp4 | 5.3 MB | ||
| 31 - Preprocess the base image style reference image and combination image.mp4 | 9.4 MB | ||
| 32 - Perform the style transfer optimization loop.mp4 | 12.8 MB | ||
| 33 - Save and display the final generated image.mp4 | 10.4 MB | ||
| 4 - Why Should we Learn.mp4 | 36 MB | ||
| 5 - Applications.mp4 | 44.6 MB | ||
| 6 - Why Keras and Python.mp4 | 7.8 MB | ||
| 7 - Why Google Colab.mp4 | 6 MB | ||
| 8 - Setup the Working Directory.mp4 | 8.7 MB | ||
| 8 - code.ipynb | 616.1 KB | ||
| 8 - content-image.jpg?042148 | 170.6 KB | ||
| 8 - style-image.jpg?042148 | 43.3 KB | ||
| 9 - Contents in Directory.mp4 | 14.2 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 38 total files | |||
Mastering Neural Style Transfer: Tensorflow, Keras & Python
https://DevCourseWeb.com
Published 7/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 373.80 MB | Duration: 1h 22m
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.
Requirements
Familiarity with Python programming language (basic knowledge is sufficient)
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