Generative Ai (English Version): Unleashing Next-Gen Ai
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.86 GB | Duration: 7h 19m
The Good, the Bad and the Ugly
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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