Master Generative AI with PyTorch: Build GANs Like a Pro
Focused View
13:16:28
1 -Content.mp4
08:06
1 -Source Codes & Data.zip
2 -Course Information.mp4
03:34
1 -Definition and Scope.mp4
07:10
2 -Historical Concepts and Evolution.mp4
04:04
3 -Applications.mp4
06:13
4 -Generative Architectural Networks.mp4
05:48
1 -Basics of Generative Adversarial Network (GAN).mp4
06:26
2 -Frontend and Backend of Generative Adversarial Network (GAN).mp4
24:08
1 -Introduction to Google Colab Environment.mp4
09:10
2 -Basics of Neural Network-Part 1.mp4
06:17
3 -Basics of Neural Network-Part 2.mp4
07:17
4 -Basics of Neural Network-Part 3.mp4
07:09
5 -Basics of Neural Network-Part 4.mp4
09:03
6 -Basics of Long Short-Term Memory (LSTM)-Part1.mp4
06:01
7 -Basics of Long Short-Term Memory (LSTM)-Part2.mp4
12:53
8 -Basics of Convolutional Neural Network-Part1.mp4
04:57
9 -Basics of Convolutional Neural Network-Part2.mp4
16:08
10 -Basics of Convolutional Neural Network-Part3.mp4
07:09
11 -Basics of Convolutional Neural Network-Part4.mp4
02:34
12 -Basics of Convolutional Neural Network-Part5.mp4
03:15
13 -Introduction to Tensors-Part1.mp4
07:00
14 -Introduction to Tensors-Part2.mp4
16:55
15 -How to Install CUDA and Set GPU (If not using Colab).mp4
08:18
1 -Data Preprocessing and Preparation-Part1.mp4
13:23
2 -Data Preprocessing and Preparation-Part2.mp4
15:02
3 -GenBlock Development.mp4
15:36
4 -Generator Development.mp4
17:44
5 -DisBlock Development.mp4
09:23
6 -Discriminator Development.mp4
17:31
7 -Models Initialization.mp4
09:16
8 -Develop Training Loop Data Preparation.mp4
11:49
9 -Develop Training Loop Train Discriminator.mp4
19:05
10 -Develop Training Loop Train Generator.mp4
08:18
11 -Visualization and Image Generation.mp4
33:32
1 -Synthetic Data Generation Concept.mp4
10:51
2 -Data Preparation.mp4
37:40
2 -temperature data.csv
3 -Generator Development.mp4
23:20
4 -Discriminator Development.mp4
11:01
5 -Models Initialization.mp4
12:28
6 -Develop Training Loop Data Preparation.mp4
12:09
7 -Develop Training Loop Train Discriminator.mp4
10:23
8 -Develop Training Loop Train Generator.mp4
08:09
9 -Training Process Evaluation.mp4
15:10
10 -Generate Synthetic Data.mp4
24:15
1 -Conditional GANs Concept.mp4
06:38
2 -Data Preparation Part1.mp4
26:06
2 -imagelabels.zip
3 -Data Preparation Part2.mp4
24:47
4 -Data Preparation Part3.mp4
14:44
5 -Conditional Generator Development.mp4
25:47
6 -Conditional Discriminator Development.mp4
16:11
7 -Models Initialization.mp4
04:32
8 -Develop Training Loop Data Preparation.mp4
09:44
9 -Develop Training Loop Train Discriminator.mp4
09:46
10 -Develop Training Loop Train Generator.mp4
26:27
11 -Generate and Display Image Based on Users Input.mp4
26:20
1 -Revising Conditional Generator.mp4
17:27
2 -Revising Conditional Discriminator & Training Loop.mp4
47:59
3 -Generate and Display Image.mp4
06:02
1 -Ethics in AI Models and Conclusion.mp4
08:18
More details
Course Overview
This hands-on course teaches you to master Generative Adversarial Networks (GANs) using PyTorch, from fundamental concepts to advanced text-to-image generation techniques. Gain practical experience through four real-world projects.
What You'll Learn
- Build and train GAN models from scratch using PyTorch
- Create synthetic data for images and time-series applications
- Implement advanced techniques like Conditional GANs for text-to-image generation
Who This Is For
- Aspiring Data Scientists and ML Engineers
- Python developers interested in AI
- Researchers and practitioners in Generative AI
Key Benefits
- Hands-on projects with real-world applications
- Comprehensive neural network foundation included
- Ethical considerations for responsible AI development
Curriculum Highlights
- GAN Fundamentals & Neural Network Basics
- Image Generation with DCGANs
- Text-to-Image Conversion with Conditional GANs
Focused display
Category
- language english
- Training sessions 59
- duration 13:16:28
- Release Date 2025/06/02
