Mastering Image Generation with GANs using Python and Keras

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Karthik K

1:34:05

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1. Fundamentals

1. Introduction.mp4

02:49

2. About this Project.mp4

03:13

3. Why should we learn.mp4

02:16

4. Applications.mp4

03:04

5. Why Python and Keras.mp4

02:27

6. Why Google Colab.mp4

03:07

2. Model Building and Training

1.1 cats.zip

1. Download Dataset.mp4

02:38

2.1 code.zip

2. Python Code.mp4

01:21

3. Activate GPU.mp4

02:11

4. Current Status of GPU.mp4

01:34

5. Mounts Google Drive to a Google Colab notebook.mp4

01:59

6. Importing libraries and Modules.mp4

02:49

7. Enabling NumPy-like Behavior in TensorFlow.mp4

02:15

8. Setting the Random Seed.mp4

01:59

9. Setting up a Directory.mp4

01:41

10. Visualizing Large Datasets of Images.mp4

02:07

11. Settings for the Training Process.mp4

02:59

12. Sets the Number of Training Samples.mp4

01:39

13. Training Images are Loaded and Preprocessed.mp4

02:21

14. List of Preprocessed Images Converted into a Numpy Array.mp4

01:49

15. Normalizing the pixel values.mp4

01:35

16. Creating a Shuffled and Batched TensorFlow Dataset.mp4

01:57

17. Define Discriminator Neural Network Model.mp4

04:19

18. Define Generator Neural Network Model.mp4

03:27

19. Generators loss.mp4

01:55

20. Loss of the Discriminator.mp4

02:34

21. Define and Summarize the Generator and Discriminator Networks.mp4

01:34

22. Define Optimizers.mp4

01:55

23. Define a Loss Function.mp4

02:13

24. Visualize the Generated Images.mp4

02:33

25. Create and Save Checkpoints During Training.mp4

01:28

26. Value of the latent dim Hyperparameter.mp4

01:44

27. Generating a Tensor of Random Noise.mp4

01:32

28. Define a TensorFlow training step.mp4

01:50

29. Plot Training Metrics During the GAN Training.mp4

02:41

30. Define Training Function.mp4

01:53

31. Function Generates Images Using the Generator Model.mp4

02:19

32. Training.mp4

02:09

33. Creates an Animated GIF.mp4

02:35

34. Generate and Visualize Images from the Generator.mp4

02:48

35. Showing Examples of Images Generated by the GAN Model.mp4

02:46

Description

Hands-On Image Generation with Generative Adversarial Networks (GANs) using Python, TensorFlow, & Keras in Google Colab

What You'll Learn?

Understand the fundamentals of Generative Adversarial Networks (GANs) and their applications in image generation.
Gain a comprehensive understanding of the architecture and components of GANs.
Learn how to implement GANs using Python and Keras, a popular deep learning framework.
Acquire the knowledge and skills to train and evaluate GAN models for image generation tasks.
Gain hands-on experience through practical project.
Apply learned concepts and techniques to real-world image generation problems and datasets.

Who is this for?

Those who have a keen interest in machine learning and want to expand their knowledge and skills in generative models, specifically GANs.

Professionals who work in the field of data science, artificial intelligence, or related domains and want to gain expertise in generating realistic images using GANs.

Students pursuing computer science or related fields who want to enhance their understanding of advanced machine learning techniques and apply them to image generation tasks.

Software developers or programmers who want to delve into the exciting field of generative models and explore how GANs can be used to create novel and realistic images.

Individuals engaged in research or innovation, particularly in the areas of computer vision, image processing, or generative models, who want to leverage GANs for generating new visual content.

What You Need to Know?

Some experience with Python programming will be helpful as the course extensively uses Python for implementing GANs.

More details

Description
In this comprehensive course, you will dive into the fascinating world of image generation using Generative Adversarial Networks (GANs) and gain hands-on experience in implementing these powerful models using Python, TensorFlow, and Keras. GANs have revolutionised the field of artificial intelligence and are widely used in various domains such as computer vision, art, entertainment, and more.
Throughout the course, you will learn the fundamental concepts and principles behind GANs, including how they work, their components, and their training process. You will explore DCGAN architecture to generate high-quality and realistic images from random noise. You will also understand the challenges and considerations involved in training GANs effectively.
Through practical coding exercises and projects, you will gain proficiency in Python programming, TensorFlow, and Keras libraries. You will develop a deep understanding of how to build, train, and evaluate GAN models for image generation tasks. Additionally, you will learn how to leverage Google Colab, a powerful cloud-based development environment, to harness the capabilities of GPUs for accelerated training.
By the end of this course, you will have a strong foundation in GANs and image generation techniques, enabling you to embark on exciting projects and explore various applications in fields such as computer graphics, creative arts, advertising, and even research. The skills and knowledge you acquire throughout the course will equip you with a valuable asset sought after by industries that rely on computer vision and artificial intelligence, increasing your job prospects in roles related to machine learning, computer vision, data science, and image synthesis.
Join us on this immersive learning journey to unlock your creativity and become proficient in image generation with GANs, empowering you to stand out in the competitive job market and opening doors to exciting career opportunities.
Who this course is for:
Those who have a keen interest in machine learning and want to expand their knowledge and skills in generative models, specifically GANs.
Professionals who work in the field of data science, artificial intelligence, or related domains and want to gain expertise in generating realistic images using GANs.
Students pursuing computer science or related fields who want to enhance their understanding of advanced machine learning techniques and apply them to image generation tasks.
Software developers or programmers who want to delve into the exciting field of generative models and explore how GANs can be used to create novel and realistic images.
Individuals engaged in research or innovation, particularly in the areas of computer vision, image processing, or generative models, who want to leverage GANs for generating new visual content.

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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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