Generative Ai: All-In-One Fast Track - All Levels

Generative Ai: All-In-One Fast Track - All Levels - Printable Version

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Generative Ai: All-In-One Fast Track - All Levels - AD-TEAM - 11-29-2024

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Generative Ai: All-In-One Fast Track - All Levels
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 835.29 MB | Duration: 3h 23m

Learn what is Generative AI | The fundamental concepts underneath | How does it generates images and text | fast track

[b]What you'll learn[/b]

Understand Generative AI Fast: Learn how Generative AI differs from traditional AI in a concise, accessible way.

Explore Real-World Applications: Discover how AI generates text, images, audio, and video with real-life examples and short demos.

Master Foundational Concepts: Gain clarity on terms like datasets, training datasets, test datasets, and key stages in training.

Dive into Neural Networks: Learn the basics of Neural Networks and Convolutional Neural Networks for image and data processing

Unpack Advanced Architectures that power text and image generation : Explore GANs, VAEs, and Transformers at a conceptual level without the complex math.

Ethics and Limitations: Learn about ethical considerations and challenges in Generative AI to use these tools responsibly.

[b]Requirements[/b]

Curiosity about AI - No prior technical knowledge needed, but a strong interest in understanding AI and Generative AI is essential.

Basic Math - Additions and multiplications

Openness to Concepts - Willingness to grasp new technical terms, from foundational AI concepts to advanced architectures.

No Programming Required - This course does not require coding skills, as the focus is on theory

[b]Description[/b]

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:AI Fundamentals & Generative AI Differences: Grasp the basics of AI and understand what sets Generative AI apart.Generative AI Applications: Explore applications of AI to generate text, images, audio, and video.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.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

Overview

Section 1: Introduction

Lecture 1 Course introduction

Lecture 2 Your instructor

Section 2: Introduction to AI and Generative AI

Lecture 3 Artificial Intelligence

Lecture 4 Brief history of AI

Lecture 5 Generative AI and comparison with AI

Lecture 6 Gen AI Global Impact

Lecture 7 Section 2 Summary

Section 3: Types of Generative AI (principles, demo, applications)

Lecture 8 Section introduction

Lecture 9 Generative AI for Text

Lecture 10 Demo: Text generation

Lecture 11 Popular tools (text)

Lecture 12 Generative AI for Images

Lecture 13 Demo: Image generation

Lecture 14 Popular tools (image)

Lecture 15 Generative AI for Audio

Lecture 16 Demo: Audio generation

Lecture 17 Applications (audio)

Lecture 18 Generative AI for Video

Lecture 19 Applications (video)

Lecture 20 Multimodal Gen AI

Lecture 21 Section 3 Summary

Section 4: Foundational Concepts and Key Terminology

Lecture 22 The Generative AI framework

Lecture 23 The Generative AI framework - key takeway

Lecture 24 Generative AI in Deep Learning

Lecture 25 Neural networks (fundamentals)

Lecture 26 Convolutional Neural networks (fundamentals)

Lecture 27 Section 4 Summary

Section 5: Generative Adversarial Network Model

Lecture 28 Introduction to GANs

Lecture 29 Deep dive objectives

Lecture 30 Dataset preparation

Lecture 31 Training the Discriminator

Lecture 32 Training the Generator - Equilibrium

Lecture 33 GANs Flash Card (+ Evaluation and Inference)

Section 6: Introduction to deep dives

Lecture 34 What to expect in the deep dive sections

Section 7: Transformer Model

Lecture 35 Introduction to transformers

Lecture 36 Deep dive objectives

Lecture 37 Tokens and Embeddings

Lecture 38 Positional Encoding

Lecture 39 Normalisation layer

Lecture 40 Data preprocessing principles (Tokenization, vocabulary, masking)

Lecture 41 Self-attention Mechanism

Lecture 42 Self-Attention Mechanism - Attention vectors (Q,K,V) /Attention scores

Lecture 43 Self-Attention Mechanism - Context sharing

Lecture 44 Residual connection

Lecture 45 Normalising the attention scores

Lecture 46 Feed forward layer

Lecture 47 Residual and Normalisation

Lecture 48 Multi-head attention

Lecture 49 Decoder : Output - Embedding - Positional Encoding - Normalisation

Lecture 50 Decoder : Masked multi-head attention - Residual C. - Normalisation

Lecture 51 Decoder: Multi-headed Cross-Attention + residual and normalisation (Decoder)

Lecture 52 Decoder : Feed forward Network - Residual C. -Normalisation

Lecture 53 Decoder: Linear & Softmax layers

Lecture 54 Decoder: Learning consolidation (using the decoder)

Lecture 55 Transformers- Flash card

Lecture 56 Transformers Flash Card + Evaluation and Inference

Section 8: Variational Auto Encoders

Lecture 57 VAE-Introduction to VAEs

Lecture 58 VAE-Deep dive objectives

Lecture 59 VAE-Dataset preparation

Lecture 60 VAE-Encoder - Input Layer

Lecture 61 VAE-Encoder - First Conv2D layer

Lecture 62 VAE-Encoder - Second Conv2D layer

Lecture 63 VAE-Encoder - Third Conv2D layer

Lecture 64 Encoder - Flatten layer and Dense Layer

Lecture 65 Encoder - Dense Layer

Lecture 66 Encoder - Latent Space

Lecture 67 Decoder

Lecture 68 VAEs Flash Card (+ Evaluation and Inference)

Section 9: Trust and Limitations of AI

Lecture 69 Trust and Limitations of AI

Lecture 70 True or Myth? What's Legal to Do with Generative AI

Lecture 71 Use GEN AI Responsibly

Section 10: Final Thougts & Thank you

Lecture 72 Final thoughts

Lecture 73 Thank you

Busy Professionals: Ideal for those who want a fast-track introduction to Generative AI concepts without unnecessary complexity.,AI Newcomers: Perfect for anyone curious about AI trends, applications, and key technologies.,Tech Enthusiasts: For those who want to understand the AI models driving innovation in text, image, and data generation.,Lifelong Learners: Designed for people with no coding background who are eager to explore the possibilities of AI

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