Generative AI for beginners is a term used by people who have minimal experience with artificial intelligence. Aren’t you fascinated by the vast possibilities and features of Generative AI technologies? The positive changes it can bring to the way we used to interact with technologies are phenomenal.
If you want to delve into the realm of generative AI for beginners and collect some productive tutorials then we are starting to cover every fundamental and principle of generative AI in this tutorial blog.
What is Generative AI for Beginners?
The context here is how Generative AI for beginners achieves its aim of being accessible and productive for every community. We as beginners start interacting with Artificial Intelligence to know its features and possibilities for the coming generation.
The future holds an infinite number of possibilities with Generative AI and its sub-modules like machine learning, deep learning, etc. Start your journey with Generative AI for Beginners Course and Know how to put them to work.
How Generative AI Is Trending?
The Generative AI trend might be influenced by the introduction of Generative AI models such as Open AI ChatGPT models, DALLE-E, and Google Bard. All these Generative AI Large Language Models can create unique content ideas and solutions to user queries within seconds.
- ChatGPT: It is a conversational AI model used to create dynamic, context-aware solutions while solving complex queries. It gained immense popularity by its AI features, which enable users to interact with and fine-tune chatbot responses with interactive feedback. GPT 4.0 is the latest update on open AI with the power of generative AI features.
- Google Bard: This generative AI model is offered by Google for creating interactive dialogues, research, and creative writing. The main objective of Bard was to advance transformer AI techniques for language processing, analysis, and other content types. It is not an open-source interface model available for public interface.
- DALLE-E: This powerful generative AI model creates unique and detailed images based on textual descriptions. This generative AI model was developed by Open AI in 2021. DALLE-E can establish connections between various media forms such as vision, text, audio, etc. It can create custom visuals of a person, place, or gadgets easily.
However, we need to learn what exactly generative AI entails. Generative AI is a tool that is based on artificial intelligence.
Generative AI Fundamentals
Generative AI is a productive tool in Artificial intelligence that possesses the capability to generate a wide range of content including text, visuals, audio, and synthetic data.
The recent surge in surrounding Generative AI from user-friendly interfaces that allow users to effortlessly create high quality text graphics and video within seconds.
- Generative AI relies on neural Networks like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based architectures like GPT, BERT, etc.
- The Generative AI models are trained on large datasets to learn patterns, structure, and features of input data to generate realistic outputs.
- Generative AI uses probability distributions to predict and generate unique content. It is a powerful tool for content creation using natural language processing,and simulation tasks.
Certification of Generative AI for Beginners
Get lessons from PwC experts and sharpen your knowledge through practical illustrations and examples. Hands-on knowledge with real-world scenarios based on case studies from PwC India. Enhance your Skills to boost your package based on your expertise and knowledge in the field.
Enrol in regular masterclasses arranged by experts from PwC India. The complete course is based on the latest industry curriculum provided by the leading company in the sector. Generative AI Bootcamp is an online program offered by PW Skills.
What is the Generative AI Principle and Working?
Generative AI involves utilizing AI technology for submitting data through API or a complex process that must have proven experience and expertise with specialized tools and writing applications using Large Language Models.
Generative AI Principle
The Generative AI principle enables machines to create new and unique content from existing datasets. This smart model can learn patterns, relationships, features, and structures from a given dataset.
It can generate new and similar unique content through probabilistic reasoning. Generative AI Produces the most unique and likely outcome based on the pre-trained dataset.
Generative AI Working
Generative AI for beginners must go through the working of artificial models based on large datasets, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer models.
- Gen-AI Training: The AI is trained on vast amounts of data to understand underlying patterns. For example, for text generation, it learns grammar, style, and context. For images, it learns shapes, textures, and objects.
- Data Representation: The AI creates an internal representation of the data through neural networks, encoding essential features and patterns.
- Generation: Once trained, the model generates new content. It does so by sampling from the learned distribution of data. For text, it predicts the next word based on previous words; for images, it generates new pixels based on learned features.
- Refinement: In some models, like GANs, the generated content is refined through feedback mechanisms (e.g., a discriminator network in GANs evaluates and improves the generated output).
Applications of Generative AI for Beginners
There are many use cases of Generative AI models in the real world. Check some of the following applications below.
- Content Creation: Generate text, articles, or captions using tools like ChatGPT.
- Image Generation: Create unique artwork, designs, or illustrations with tools like DALL-E.
- Code Assistance: Write and debug code using AI-powered tools like GitHub Copilot.
- Music Composition: Compose music or soundtracks through platforms like AIVA.
- Chatbots: Build conversational agents for customer support or personal use.
- Style Transfer: Transform photos into artistic styles or mimic famous artworks.
- Personalized Recommendations: Use AI to suggest products, movies, or books.
- Education: Summarize lessons, explain concepts, or create quizzes with AI tools.
- Data Augmentation: Expand datasets by generating synthetic examples for training AI models.
- Gaming: Create characters, storylines, or game environments using generative models.
Generative AI Bootcamp by PW Skills
The Generative AI Bootcamp by PW Skills is divided into over ten modules in the course. Generative AI for beginners consist of curriculum guides for freshers and working professionals.
Module 1: Introduction to Artificial Intelligence
- What is AI?
- Historical Development and Key Milestones
- Distinction between AI, Machine Learning and Deep Learning
- Understanding What makes AI “Generative”?
- How does Generative AI fit into the AI Ecosystem?
- Overview of AI subfields
- What sets Generative AI apart from other forms?
- Essential Terminology: Algorithms, Neural networks Models
Module 2: Evolution of Generative AI
- Early AI systems: Rule Based AI
- The Rise of Machine Learning
- Emergence of Generative AI
- Deep Learning Role in Advanced Gen AI
- Evolution of Large Language Model from GPT 2 to GPT 4.
- Transformative Role of Pre-trained models
Module 3: Understanding Generative AI
- Generative AI Insights
- Capabilities of Generative AI
- Key Generative Models
- What is the role of Generative AI Models
- Generative AI vs Traditional AI
- What does Generative AI do?
Module 4: How Generative AI Works?
- Basics of AI Models
- Training Gen AI Models
- Prompt Engineering
Module 5: Overview of Generative AI tools
- Text Based Tools: ChatGPT
- Image Based Tools: DALLE-E
- Video and Audio Based Tools: Runway ML, Fish Audio, ElevenLabs
- AI-Based Research: AI-Driven research and information summarization
Module 6: Applications of Generative AI models Across Different Sectors
- Marketing and Advertising
- Content Creation
- Design and Art
- Customer Service Support
- Education and Learning
- Software Development
- Exclusive Case Studies by PwC
Module 7: Prompt Engineering
- What is Prompt Engineering?
- Effective Prompt Structuring
- Advanced Prompting Techniques
- Prompt Iteration
- Hands on Case Studies based on Text, Images, audio, and Video
Module 8: Overview of Generative AI and Features
- Understanding Generative AI features
- Retrieval Augmented Generation (RAG)
- Architectural Components
- Langchain and Langraph
- Vector Databases
Module 9: Ethical and Logical Implications of Generative AI
- Ethical Consideration of Generative AI
- Regulation and Governance
- Ethical Solutions
- Live Lessons
Module 10:
- Exclusive Masterclasses by PwC on Generative AI and Case Studies.
Generative AI for Beginners with PW Skills
Master generative AI Course skills and explore a wide range of career opportunities in the field. Develop generative ai models while working with powerful gen AI models like BARD, DALLE-E, GPT, etc.
Learn Artificial Intelligence, machine learning, deep learning, Prompt engineering, and much more. Get complete access to PwC real-world case projects and collect hands-on experience with industry experts only at pwskills.com
Generative AI for Beginners FAQs
Q1. What is Generative AI?
Ans: Generative AI is a branch of artificial intelligence which help create new content like text, images, audio, and videos by learning patterns, relationships from the pre trained data.
Q2. Does Generative AI follow ethics and rules?
Ans: Generative AI works beyond a set of algorithms and formulas. However, the main objective of Generative AI is to bring sustainable development in all fields and help technologies interact faster and better.
Q3. What is the principle of Generative AI for beginners?
Ans: The Generative AI model operates on the principle of using deep learning models to analyze data, identify patterns or relationships and generate output based on learning from the pre-trained data.
Q4. What is the work behind Generative AI?
Ans: Generative AI works on extensive data training models based on GANs, and VAEs which help them understand the characteristics of data and produce reliable and unique outputs.
