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

Generative AI (English Version): Unleashing Next-Gen AI, available at $69.99, has an average rating of 4.1, with 59 lectures, based on 72 reviews, and has 4121 subscribers.

You will learn about 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 This course is ideal for individuals who are AI/ML Practitioners, Developers, Engineers and Researchers or NLP Engineers or Researchers or CV Engineers or Researchers or Data Scientists It is particularly useful for AI/ML Practitioners, Developers, Engineers and Researchers or NLP Engineers or Researchers or CV Engineers or Researchers or Data Scientists.

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Summary

Title: Generative AI (English Version): Unleashing Next-Gen AI

Price: $69.99

Average Rating: 4.1

Number of Lectures: 59

Number of Published Lectures: 59

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

Hello and Welcome to a new Journey in the vast area of Generative AI

Generative 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course overview

Chapter 2: What is Generative AI?

Lecture 1: What is Generative AI?

Lecture 2: Generative vs. Discriminative models

Lecture 3: Why Generative models?

Lecture 4: Encoder-Decoder design pattern

Lecture 5: GenAI modalities mappings

Chapter 3: Txt2Txt GenAI

Lecture 1: Unimodal mappings: Txt2txt and Language models

Lecture 2: Statistical Language Models (SLM)

Lecture 3: Neural Language Models (NLM) – Char level

Lecture 4: Neural Language Models (NLM) – Word level

Lecture 5: SLM and NLM in Python and Keras

Lecture 6: Seq2seq models

Lecture 7: Seq2seq + Attention models

Lecture 8: Transformers

Lecture 9: Huggingface Transformer Pipeline

Lecture 10: Large-Scale Language Models (LLM) – Transfer Learning in NLP

Lecture 11: Pre-trained Transformers

Lecture 12: BERT

Lecture 13: GPT

Lecture 14: ChatGPT

Lecture 15: OpenAI API

Lecture 16: GPT-3 Finetuning

Lecture 17: GPT-3 Chatbot

Lecture 18: ChatGPT Clone in Google Colab

Lecture 19: ChatGPT Clone in Streamlit

Lecture 20: ChatGPT Clone Excercise

Chapter 4: Img2Img GenAI

Lecture 1: Img2Img Encoder-Decoder

Lecture 2: Auto Encoder (AE)

Lecture 3: AE Visualization

Lecture 4: Variational Auto Encoder (VAE)

Lecture 5: Conditional VAE

Lecture 6: Coding AE in Keras

Lecture 7: Generative Adversarial Nets (GANs)

Lecture 8: Generating images from GANs

Lecture 9: Training GANs

Lecture 10: Coding GAN training in Keras

Lecture 11: DCGAN

Lecture 12: Conditional GANs

Lecture 13: AttributeGAN

Lecture 14: How Good are GANs today?

Lecture 15: Domain adaptation with pix2pix and CycleGAN

Chapter 5: Multi-modal GenAI

Lecture 1: Multimodal Txt2Img generation

Lecture 2: Diffusion models

Lecture 3: Latent Diffusion Models (LDM)

Lecture 4: CLIP

Lecture 5: StableDiffusion

Lecture 6: Online tools for txt2img: DreamStudio and Midjourney

Lecture 7: OpenAI API – DALL-E

Lecture 8: Huggingface – StableDiffusion

Lecture 9: Excercise – Midjourney clone

Lecture 10: Img2Txt generation – Image Captioning

Lecture 11: Txt2Voice generation – VALL-E

Chapter 6: The good, the bad and the ugly

Lecture 1: The Good

Lecture 2: The Bad

Lecture 3: The Ugly

Lecture 4: What should we do?

Chapter 7: Conclusion

Lecture 1: Conclusion

Chapter 8: Material

Lecture 1: Material

Instructors

Coursat.ai Dr. Ahmad ElSallab
Practical AI Courses

Rating Distribution

1 stars: 1 votes
2 stars: 4 votes
3 stars: 10 votes
4 stars: 30 votes
5 stars: 27 votes

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