Generative Ai (English Version): Unleashing Next-Gen Ai

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What you'll learn
Generative AI definition, areas of applications, mappings like txt2txt, img2txt, txt2img and txt2voice
How ChatGPT works, and the underlying tech behind like GPT, Large-Scale Language Models (LLM) and Transformers
How Latent Diffusion, StableDiffusion and DALL-E systems work
Generative Adversarial Networks (GANs) and Variational Auto Encoder (VAE)
The good, bad and ugly faces of GenAI, and how to adapt to the new tech
Build ChatGPT clone using OpenAI API and Streamlit
Build NLP applications using OpenAI API like Summarization, Text Classification and fine tuning GPT models
Build NLP applications using Huggingface transformers library like Language Models, Summarization, Translation, QA systems and others
Build Midjourney clone application using OpenAI DALL-E and StableDiffusion on Huggingface

Requirements
AI, ML and Deep Learning foundations
NLP: RNN, LSTM, Transformers basics
CV: ConvNets

Description
Hello and Welcome to a new Journey in the vast area of Generative AIGenerative AI is changing our definition of the way of interacting with machines, mobiles and computers. It is changing our day-to-day life, where AI is an essential component.This new way of interaction has many faces: the good, the bad and the ugly.In this course we will sail in the vast sea of Generative AI, where we will cover both the theoretical foundations of Generative models, in different modalities mappins: Txt2Txt, Img2Txt, Txt2Img, Img2Txt and Txt2Voice and Voice2Text. We will discuss the SoTA models in each area at the time of this course. This includes the SoTA technology of Transformers, Language models, Large LM or LLM like Generative Pre-trained Transformers (GPT), paving the way to ChatGPT for Text Generation, and GANs, VAE, Diffusion models like DALL-E and StabeDiffusion for Image Generation, and VALL-E foe Voice Generation.In addition, we will cover the practical aspects, where we will build simple Language Models, Build a ChatGPT clone using OpenAI APIs where we will take a tour in OpenAI use cases with GPT3.5 and ChatGPT and DALL-E. In addition we will cover Huggingface transformers and StableDiffusion.Hope you enjoy our journey!

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course overview

Section 2: What is Generative AI?

Lecture 3 What is Generative AI?

Lecture 4 Generative vs. Discriminative models

Lecture 5 Why Generative models?

Lecture 6 Encoder-Decoder design pattern

Lecture 7 GenAI modalities mappings

Section 3: Txt2Txt GenAI

Lecture 8 Unimodal mappings: Txt2txt and Language models

Lecture 9 Statistical Language Models (SLM)

Lecture 10 Neural Language Models (NLM) - Char level

Lecture 11 Neural Language Models (NLM) - Word level

Lecture 12 SLM and NLM in Python and Keras

Lecture 13 Seq2seq models

Lecture 14 Seq2seq + Attention models

Lecture 15 Transformers

Lecture 16 Huggingface Transformer Pipeline

Lecture 17 Large-Scale Language Models (LLM) - Transfer Learning in NLP

Lecture 18 Pre-trained Transformers

Lecture 19 BERT

Lecture 20 GPT

Lecture 21 ChatGPT

Lecture 22 OpenAI API

Lecture 23 GPT-3 Finetuning

Lecture 24 GPT-3 Chatbot

Lecture 25 ChatGPT Clone in Google Colab

Lecture 26 ChatGPT Clone in Streamlit

Lecture 27 ChatGPT Clone Excercise

Section 4: Img2Img GenAI

Lecture 28 Img2Img Encoder-Decoder

Lecture 29 Auto Encoder (AE)

Lecture 30 AE Visualization

Lecture 31 Variational Auto Encoder (VAE)

Lecture 32 Conditional VAE

Lecture 33 Coding AE in Keras

Lecture 34 Generative Adversarial Nets (GANs)

Lecture 35 Generating images from GANs

Lecture 36 Training GANs

Lecture 37 Coding GAN training in Keras

Lecture 38 DCGAN

Lecture 39 Conditional GANs

Lecture 40 AttributeGAN

Lecture 41 How Good are GANs today?

Lecture 42 Domain adaptation with pix2pix and CycleGAN

Section 5: Multi-modal GenAI

Lecture 43 Multimodal Txt2Img generation

Lecture 44 Diffusion models

Lecture 45 Latent Diffusion Models (LDM)

Lecture 46 CLIP

Lecture 47 StableDiffusion

Lecture 48 Online tools for txt2img: DreamStudio and Midjourney

Lecture 49 OpenAI API - DALL-E

Lecture 50 Huggingface - StableDiffusion

Lecture 51 Excercise - Midjourney clone

Lecture 52 Img2Txt generation - Image Captioning

Lecture 53 Txt2Voice generation - VALL-E

Section 6: The good, the bad and the ugly

Lecture 54 The Good

Lecture 55 The Bad

Lecture 56 The Ugly

Lecture 57 What should we do?

Section 7: Conclusion

Lecture 58 Conclusion

Section 8: Material

Lecture 59 Material

AI/ML Practitioners, Developers, Engineers and Researchers,NLP Engineers or Researchers,CV Engineers or Researchers,Data Scientists

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