Generative AI: All-in-one Fast Track - All levels
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- Curriculum
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Course Description:
Are you curious about AI or looking to deepen your understanding of Generative AI? This course is crafted for tech professionals, students, and even sales roles in tech companies, designed to take you from AI fundamentals to advanced concepts in an accessible and modular way.
With 10 years of experience as a solution architect at major American tech companies like AWS, VMware, and Dell, I’m passionate about technology—especially AI. Over the years, I’ve delivered presentations to diverse audiences, making complex topics engaging and understandable. I bring that experience here, guiding you step-by-step through the essentials and beyond.
What You’ll Learn:
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AI Fundamentals & Generative AI Differences: Grasp the basics of AI and understand what sets Generative AI apart.
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Generative AI Applications: Explore applications of AI to generate text, images, audio, and video.
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Advanced Concepts Simplified: Understand the jargon, around AI, down to GEN AI, the fundamental technics behind Deep Learning, and dive into three popular Generative AI models: Transformers, GANs, and VAEs where we dismantle their architectures.
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Myths, Ethics & Legalities: Uncover common myths about Generative AI’s ethical and legal challenges and discover how to use AI responsibly, following US and EU guidelines.
Whether you’re starting fresh, upskilling for a tech role, or enhancing your knowledge for a career in tech sales, this course has everything you need to get started and go further. I intentionally skip some expert concepts to maintain this course at the right level for a new (or fairly new audience) looking for fast track).
If you like this course, please share feedback, and if you need to reach out please use my Linked-in or send me a message here.
Happy learning,
Christelle
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Section introduction -
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Generative AI for Text
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Demo: Text generation
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Popular tools (text)
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12
Generative AI for Images
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13
Demo: Image generation
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14
Popular tools (image)
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15
Generative AI for Audio
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Demo: Audio generation
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17
Popular tools (audio)
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Generative AI for Video
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Popular tools (videos)
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Multimodal Gen AI
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21
Section 3 Summary
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35
Introduction to transformers
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36
Deep dive objectives
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37
Data preprocessing principles (Tokenization, vocabulary, masking)
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38
Tokens and Embeddings
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39
Positional Encoding
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40
Normalisation layer
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41
Self-attention Mechanism
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42
Self-Attention Mechanism - Attention vectors (Q,K,V) /Attention scores
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43
Self-Attention Mechanism - Context sharing
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44
Residual connection
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45
Normalising the attention scores
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46
Feedforward layer
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47
Residual and Normalisation
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48
Multi-head attention
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49
Decoder : Output - Embedding - Positional Encoding - Normalisation
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50
Decoder : Masked multi-head attention - Residual C. - Normalisation
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Decoder: Multi-headed Cross-Attention + residual and normalisation (Decoder)
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Decoder : Feed forward Network - Residual C. -Normalisation
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53
Decoder: Linear & Softmax layers
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54
Decoder: Choosing the next word (learning consolidation)
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55
Transformers Flash Card + Evaluation and Inference
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56
VAE-Introduction to VAEs
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57
VAE-Deep dive objectives
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58
VAE-Dataset preparation
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59
VAE-Encoder - Input Layer
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60
VAE-Encoder - First Conv2D layer
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61
VAE-Encoder - Second Conv2D layer
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62
VAE-Encoder - Third Conv2D layer
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Encoder - Flatten layer and Dense Layer
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64
Encoder - Dense Layer
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65
Encoder - Latent Space
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Decoder
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67
VAEs Flash Card (+ Evaluation and Inference)
