
This video tells about topics to covered in Generative AI course.
This video covers Basics of machine learning, data in machine learning.
This video will cover Supervised learning, Unsupervised learning , advantages and disadvantages of ML.
This video will cover ML life cycle, Exploratory data analysis , ML Challenges and libraries.
This video tells about basics of deep learning, types of Deep Learning Models, Benefits of using deep learning models, Deep learning applications.
This video tells about Convolutional Neural Network (CNN), Layers in a Convolutional Neural Network,Applications of CNN.
This video tell about Neural Networks,Types of Neural Networks, the architecture of an artificial neural network, advantages , disadvantages of Neural Networks.
This video tells about basics of computor vision, Computer Vision Examples, Applications of Computer Vision,
Challenges and Limitations and Fundamental Concepts and Technologies.
This video tells what is expert system,Components of an expert system,Expert Systems way of working,Advantages of expert systems and Limitations of Expert Systems.
This video tells about GANs basics and use of GAN.
This video tells about Training & Prediction of (GANs) and Steps to Implement a Basic GAN.
This video tells about basics of transformers, Key Steps in the Transformer Process and its application in airtficial intelligence.
This video tells about RNN basics, types of RNN,RNN Architecture,Advantages and disadvantages of RNN,
This video tells about basics of Variational Autoencoders (VAEs),VAE applications.
This video tell about Alphacode features, basics, pros and cons.
This video tell about DALL E2 features, basics, pros and cons.
This video tell about DUET AI basics , features and used cases.
This video tell about GIThub Copiolet basics , features and used cases.
This video tell about ChatGPT 4 basics , feature, positive, negatives.
This video tell about basics of long chain, benifits and used cases.
This video tells about Pinecode GEN AI tools.
This video tell about basics of Synthesia , Key features,Use cases:, Pros and cons.
This video tells about Bento M and Gradio Generative AI tools.
This video tells about Murfy AI and Copy AI tools.
This video tells about Applications of Generative AI in the Industries to used in vaious field.
This video covers about basics of generative AI , its working and the diagram for generative AI.
This video tells about definition of Generative and non Generative AI along there major difference.
This video tell about how generative AI text data is created and how Text generation uses machine learning, existing data and previous user input in generating responses.Aside Gen AI text based flow and basic generative model flow use in making generative AI model.
This video will cover Applications of ChatGPT, advantages,disadvantages of chatGPT,Chatgpt development working of ChatGPT,Capabilities of ChatGPT, chatgpt history.
This video covers process for ChatGPT for text generation.
This video will tell about different kind of generative AI models for data processing and image generation.
This video tell about basics for generative AI stable diffusion.
This video tells about differnent diffusion models, working of diffusion models,detailed process of diffusion models,Key Components of a Diffusion Model, importance of diffusion model and difference between GANs & VAE with diffusion model along diffusion model application, task , purpose, examples with industry use.
This video tell about dream studio platfrom details.
This video will tell about different process for Editing Images with stable diffusion.
This video covers about details of tips for prompt engineering for image generation,Prompting Best Practices,Types of Prompts,Prompt iteration strategies.
This video tells about challenges for Images in gen AI.
This video tells about details of Generative AI Architecture and Layers of Generative AI Architecture.
This video tells about Enterprise AI and Key Differences Between Enterprise AI & Generative AI.
This video tells about Examples and benifits of text summarization and Way of Doing Prompt Engineering for text summarization.
This video tells about information extraction in prompt egineering.
This video covers Question Answering which have been used in promot engineering.
This video tells about Advantages of Prompt Engineering in Text Classification, common rules for text classfication and different types of prompt techiques .
This video tell Principles of Prompt Engineering, How prompt engineering is employed in various tools for code generation.
This video will tell about basics of paraphasing .
This video tells reasons for measuring prompt performance, Key Metrics for Assessing Prompt Quality, future and The importance of prompt evaluation.
This course covers below mentioed topics to be complete generative AI skills:
• Machine learning basics
. Supervised and Unsupervised learning
. Generative Adversarial Network (GAN) and its application
. Variational Autoencoders, Transformers and its application
. Convolutional Neural Network , Expert systems, Recurrent neural networks
. Generative AI tools (Alphacode, DALL E2, DUET AI, GIThub Copiolet, ChatGPT4 etc.)
•Gen-AI basics
•Gen- AI and Non Gen- AI
•Gen-AI applications
•Text Data
•Gen-AT text Intro
•Chatgpt Overview
•Chatgpt - Text generation
•Google bard - Text generation
•Diffusion AI models
•Dream studio platform
•Generating Images with Stable Diffusion
•Editing Images with Stable Diffusion
•Prompt Engineering for Image Generation
•Mitigating Data Leakage using Data Masking
•Using Private Generative AI Models
•Role of Privacy by Design in AI
Aside
Gen AI use in Healthcare, Education, Tourism covered
•Potential Risks to Data Privacy in AI
•Mitigating Data Leakage using Data Masking
•Using Private Generative AI Models
•Role of Privacy by Design in AI
•Implementing a Data Privacy Culture
Genertive experts have good skills to create, innovate, and generate content. They have a deep understanding of generative models, neural networks, machine learning algorithms, and other advanced techniques that enable the creation of novel and creative outputs.
Generative AI creates new content, like text or images, based on patterns in data. Large Language Models (LLMs) are a powerful form of this AI, generating human-like text, while Small Language Models (SLMs) focus on specialized tasks with less data. Retrieval-Augmented Generation (RAG) enhances these models by pulling in external information for more accurate results.
AI Agents use generative AI to autonomously perform tasks such as writing or research. Together, they represent cutting-edge advancements in automation and creativity.