Mastering Generative AI with PyTorch: Hands-on Experience

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Navid Shirzadi

13:16:28

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1 - Course Content & Info

1 -Content.mp4

08:06

1 -Source Codes & Data.zip

2 -Course Information.mp4

03:34

2 - Introduction GEN AI

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

3 - Generative Adversarial Network (GAN) Concepts

1 -Basics of Generative Adversarial Network (GAN).mp4

06:26

2 -Frontend and Backend of Generative Adversarial Network (GAN).mp4

24:08

4 - Basic Neural Network Concepts (Optional)

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

5 - Project 1, Image Generation

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

6 - Project 2, Synthetic Time Series Data Generation

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

7 - Project3, Conditional GANs Text to Image

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

8 - Project4, Conditional GANs Convolutional Layers

1 -Revising Conditional Generator.mp4

17:27

2 -Revising Conditional Discriminator & Training Loop.mp4

47:59

3 -Generate and Display Image.mp4

06:02

9 - Ethics in Gen AI

1 -Ethics in AI Models and Conclusion.mp4

08:18

Description

Hands-On Training in Generative Adversarial Networks: Create, Train, and Apply GANs with PyTorch

What You'll Learn?

Understand GAN Fundamentals
Learn to build and train GAN models from scratch using PyTorch
Acquire the skills to create synthetic data for various applications
Explore advanced GAN techniques for converting text into images

Who is this for?

Aspiring Data Scientists and Machine Learning Engineers

Python Developers with an Interest in AI

Students and Professionals in Data Science or AI

Researchers and Practitioners

Anyone Curious About Generative AI

What You Need to Know?

Basic Python Knowledge

Understanding of Machine Learning Concepts

Mathematical Background

More details

Description
Dive into the transformative world of Generative AI with this comprehensive course on Generative Adversarial Networks (GANs) using PyTorch. This course is designed to provide a deep understanding of GANs and their applications, blending theoretical knowledge with extensive hands-on experience.
What You'll Learn:
Core GAN Concepts: Grasp the fundamentals of GANs, including the dynamics between the Generator and Discriminator networks, and understand how they collaborate to create realistic outputs.
Advanced Model Development: Gain practical experience in building and training sophisticated GAN models from scratch using PyTorch. Learn to implement Convolutional Neural Networks (CNNs) for both Generator and Discriminator, and discover how to refine these models for enhanced performance.
Complex Data Generation Techniques: Explore how to integrate complex models such as Long Short-Term Memory (LSTM) networks into GAN frameworks to generate time series and sequential data. Understand the synergy between LSTMs and GANs to create high-quality synthetic data.
Text-to-Image Synthesis: Delve into advanced GAN techniques for generating images from textual descriptions. Learn how to combine textual input with visual data to produce accurate and engaging visual representations.
Ethical Considerations: Engage in discussions about the moral implications of generative AI technologies. Understand the potential impact of GANs on privacy, misinformation, and the ethical use of synthetic data.
Hands-On Coding Experience: Work on real-world projects with step-by-step guidance. Youâ€™ll write and debug code collaboratively, with detailed line-by-line explanations of the purpose and function of each line. Learn to troubleshoot and optimize your GAN models for better results.
Who Should Enroll:
This course is ideal for aspiring data scientists, machine learning engineers, and Python developers who want to expand their expertise in generative models. It is also suitable for researchers and practitioners in computer vision and those interested in the ethical dimensions of AI. Whether you're new to GANs or looking to deepen your knowledge with advanced techniques and ethical insights, this course provides the tools and understanding to apply generative AI effectively in real-world scenarios.
Join us to master GANs, leverage complex models for innovative data generation, and gain practical, hands-on experience with detailed debugging and code explanations!
Who this course is for:
Aspiring Data Scientists and Machine Learning Engineers
Python Developers with an Interest in AI
Students and Professionals in Data Science or AI
Researchers and Practitioners
Anyone Curious About Generative AI

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Python

Navid Shirzadi

Instructor's Courses

My name is Navid Shirzaid and I am super excited that you are here to read this section!I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence.To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you!

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