Master in Generative AI (Artificial Intelligence)
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Want to become a Master in Generative AI but don’t know what to do and how?
Take a look at this course where you will
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Not only learn about the Generative AI in depth interactively with a lot of examples including Gen AI Model Types, Designing, Training, Optimizing, Integrating and Deploying but also
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Telling you how can you master them to become an Excellent Generative AI Engineer
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Preview many lectures for free to see the content for yourself
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Clear your doubts on this topic any time while doing the course
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Get Udemy’s 30 days Money Back Guarantee
My first exposure to Generative happened with the advent of ChatGPT in 2022 once I got into making online courses on Udemy in 2021
Initially I used ChatGPT to supplement my knowledge on the variety of subjects. I also came across other Gen AI Models to meet the growing demand of visualization in my Udemy Courses
Over last three years I have used these Generative AI Models very actively in all walks of life and that has made me learn how they do what they do
I bring in this course my learnings from this journey and share with you how can you also Master Generative AI
Preview for yourself many lectures free. If you like the content, enroll for the course, enjoy and skill yourself to become a Master in Generative AI If don’t like the content, please message about how can we modify it to meet your expectations.
Please remember that this course comes with Udemy’s 30 days Money Back Guarantee
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2
Overview IThe purpose of this section is to give you an overview of Generative AI
At the end of this lecture, you will learn the following
•What is Generative AI?
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3
Overview IPlease answer following questions based on learnings in this lecture
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4
Overview II
The purpose of this section is to give you an overview of Generative AI
At the end of this lecture, you will learn the following
•What is role of Generative AI Engineer?
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5
Overview II
Please answer following questions based on learnings in this lecture
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6
Overview III
At the end of this lecture, you will learn the following
•An example of Generative AI
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7
Overview IV
At the end of this lecture, you will learn the following
•How to become a Generative AI Engineer?
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Overview IV
Please answer following questions based on learnings in this lecture
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9
Gen AI Models I
At the end of this lecture, you will learn the following
•Generative Adversarial Networks (GANs)
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10
Gen AI Models I
Please answer following questions based on learnings in this lecture
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11
Gen AI Models II
At the end of this lecture, you will learn the following
•Variational Autoencoders (VAEs)
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12
Gen AI Models II
Please answer following questions based on learnings in this lecture
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13
Gen AI Models III
The purpose of this section is to give you an in-depth view of Generative AI Models
At the end of this lecture, you will learn the following
•Transformer Models
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14
Gen AI Models III
Please answer following questions based on learnings in this lecture
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15
Designing Models I
At the end of this lecture, you will learn the following
•How to create and develop new generative models tailored to specific applications, such as natural language processing (NLP), image generation, music composition, or other creative tasks
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16
Designing Models I
Please answer following questions based on learnings in this lecture
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17
Designing Models II
At the end of this lecture, you will learn the following
How to create and develop new generative models tailored to specific applications, such as natural language processing (NLP), image generation, music composition, or other creative tasks
•Gather Domain Knowledge
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18
Designing Models II
Please answer following questions based on learnings in this lecture
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19
Designing Models III
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Natural Language Processing (NLP)
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20
Designing Models III
Please answer following questions based on learnings in this lecture
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21
Designing Models IV
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Image Generation
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22
Designing Models IV
Please answer following questions based on learnings in this lecture
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23
Designing Models V
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Work with image datasets like CIFAR-10, ImageNet
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24
Designing Models V
Please answer following questions based on learnings in this lecture
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25
Designing Models VI
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Music Composition
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26
Designing Models VI
Please answer following questions based on learnings in this lecture
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27
Designing Models VII
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to tokenize text, remove stop words, and handle special characters
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28
Designing Models VII
Please answer following questions based on learnings in this lecture
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29
Designing Models VIII
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to resize, normalize, and augment images
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30
Designing Models VIII
Please answer following questions based on learnings in this lecture
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31
Designing Models IX
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to convert audio to a suitable format (e.g., MIDI), segment into phrases or bars
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32
Designing Models IX
Please answer following questions based on learnings in this lecture
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33
Designing Models X
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Transformer Models (e.g., GPT-3, BERT)
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34
Designing Models X
Please answer following questions based on learnings in this lecture
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35
Designing Models XI
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Models like RNNs and LSTMs
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36
Designing Models XI
Please answer following questions based on learnings in this lecture
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37
Designing Models XII
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Image Generation using GANs
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38
Designing Models XII
Please answer following questions based on learnings in this lecture
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39
Designing Models XIII
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Image Generation using Variational Autoencoders (VAEs)
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40
Designing Models XIII
Please answer following questions based on learnings in this lecture
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41
Designing Models XIV
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Music Composition
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42
Designing Models XIV
Please answer following questions based on learnings in this lecture
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43
Designing Models XV
At the end of this lecture, you will learn the following
How to choose the Appropriate Model Architecture
•Autoencoders for other creative tasks
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44
Designing Models XV
Please answer following questions based on learnings in this lecture
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45
Designing Models XVI
At the end of this lecture, you will learn the following
•Diffusion Models for other creative tasks
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46
Designing Models XVI
Please answer following questions based on learnings in this lecture
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47
Designing Models XVII
At the end of this lecture, you will learn the following
•Model Implementation
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48
Designing Models XVII
Please answer following questions based on learnings in this lecture
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49
Designing Models XVIII
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
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50
Designing Models XVIII
Please answer following questions based on learnings in this lecture
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51
Designing Models XIX
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Techniques for Determining Layers and Units
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52
Designing Models XIX
Please answer following questions based on learnings in this lecture
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53
Designing Models XXI ( Designing Models XX intentionally missed)
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
How to choose activation functions
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54
Designing Models XXI ( Designing Models XX intentionally missed)
Please answer following questions based on learnings in this lecture
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55
Designing Models XXII
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Normalization
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56
Designing Models XXII
Please answer following questions based on learnings in this lecture
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57
Designing Models XXIII
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Regularization
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58
Designing Models XXIII
Please answer following questions based on learnings in this lecture
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Designing Models XXIV
At the end of this lecture, you will learn the following
Model Implementation
•Tailor the Model to the Task
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Designing Models XXIV
Please answer following questions based on learnings in this lecture
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Designing Models XXV
At the end of this lecture, you will learn the following
Model Architecture
•Tailoring the Model to task
•Loss Function
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Designing Models XXV
Please answer following questions based on learnings in this lecture
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63
Designing Models XXVI
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•Optimization Algorithm
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Designing Models XXVI
Please answer following questions based on learnings in this lecture
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Designing Models XXVII
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•Learning Rate Schedule
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Designing Models XXVII
Please answer following questions based on learnings in this lecture
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Designing Models XXVIII
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•How to select and tune optimization algorithms and learning rate schedules?
Tuning
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68
Designing Models XXVIII
Please answer following questions based on learnings in this lecture
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69
Designing Models XXIX
At the end of this lecture, you will learn the following
Model Training and Hyperparameter Tuning
•Hyperparameter Tuning
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Designing Models XXIX
Please answer following questions based on learnings in this lecture
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71
Designing Models XXX
At the end of this lecture, you will learn the following
How to Implement efficient training procedures
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72
Designing Models XXX
Please answer following questions based on learnings in this lecture
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73
Designing Models XXXI
At the end of this lecture, you will learn the follow
Scalability and Efficiency
•Model Pruning and Quantization
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Designing Models XXXI
Please answer following questions based on learnings in this lecture
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Designing Models XXXII
At the end of this lecture, you will learn the following
Scalability and Efficiency
•Distributed Training
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Designing Models XXXII
Please answer following questions based on learnings in this lecture
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Designing Models XXXIII
At the end of this lecture, you will learn the following
Scalability and Efficiency
•Hardware Utilization
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78
Designing Models XXXIII
Please answer following questions based on learnings in this lecture
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79
Designing Models XXXIV
At the end of this lecture, you will learn the following
Model Architecture Example
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80
Designing Models XXXIV
Please answer following questions based on learnings in this lecture
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81
Designing Models XXXV
At the end of this lecture, you will learn the following
Model Implementation
•Frameworks and Libraries
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Designing Models XXXV
Please answer following questions based on learnings in this lecture
