Generative AI – Datacrunch Academy

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Generative AI Made Easy: Start Your Generative AI Journey, Learn ChatGPT, LLM, Prompt engineering, Create GenAI Chatbot

Course Content

Overview of Generative AI

Definition and history of Generative AI

01:00:00
Importance and applications in various industries

00:00

Basics of Machine Learning and AI

Introduction to Neural Networks

Training Neural Networks

Deep Neural Networks (DNN)

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN)

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

Autoencoders

Variational Autoencoders (VAEs)

Introduction to GANs

Types of GANs and Applications

Transformer Models

Diffusion Models and Others

Setting Up the Environment

Building and Training Models

NLP Basics and Preprocessing

Text Generation and Language Models

Image Generation and Manipulation

Generative Art

Ethics in Generative AI

Emerging Trends and Research

Capstone Project Presentation

Course Review and Q&A

Student Ratings & Reviews

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What will you learn in this course?

Course Content

Overview of Generative AI

Definition and history of Generative AI

Importance and applications in various industries

Basics of Machine Learning and AI

Machine Learning vs. AI vs. Generative AI

Key concepts: algorithms, models, and data

Introduction to Neural Networks

Perceptrons and neural network architecture

Activation functions

Training Neural Networks

Backpropagation and gradient descent

Overfitting and regularization techniques

Deep Neural Networks (DNN)

Architecture of DNNs

Training deep networks

Convolutional Neural Networks (CNN)

Convolution operations

CNN architectures and applications

Recurrent Neural Networks (RNN)

RNN architecture and backpropagation through time

Applications in sequence data

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

Structure and benefits of LSTM and GRU

Use cases in natural language processing

Autoencoders

Basics and types of autoencoders

Applications in data compression and denoising

Variational Autoencoders (VAEs)

VAE architecture and mathematical foundation

Sampling and generating new data

Introduction to GANs

GAN architecture: generator and discriminator

Training dynamics and loss functions

Types of GANs and Applications

Conditional GANs, CycleGANs, StyleGANs

Applications in image synthesis, style transfer

Transformer Models

Attention mechanisms and transformer architecture

Applications in language models

Diffusion Models and Others

Diffusion models

Other advanced generative models

Setting Up the Environment

Tools and frameworks: TensorFlow, PyTorch

Basic coding and environment setup

Building and Training Models

Implementing autoencoders and GANs

Fine-tuning and optimizing models

NLP Basics and Preprocessing

Tokenization, embeddings, and preprocessing techniques

Text Generation and Language Models

Building and training language models

Applications in text generation and summarization

Image Generation and Manipulation

Techniques for image synthesis and style transfer

Generative Art

AI in creative processes: music, art, and literature

Ethics in Generative AI

Bias, fairness, and ethical implications

Responsible AI development

Emerging Trends and Research

Latest advancements in generative AI

Future research directions

Capstone Project Presentation

Project work: development and presentation of a generative AI application

Course Review and Q&A

Recap of key concepts

Open discussion and Q&A session

Student Ratings & Reviews

Power BI

Microsoft Fabric – A Deeper Dive

Digital Marketing