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What is Generative AI

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What is Generative AI

Generative AI is a type of artificial intelligence that focuses on creating new content. Vlr training provide Generative AI Training. This content can take many forms, including

Text: Writing stories, poems, articles, summaries, translations, etc.
Images: Generating realistic or artistic images, editing photos, creating designs, etc.
Audio: Composing music, creating sound effects, generating speech, etc. Video: Producing animated videos, editing existing footage, etc.
Code: Writing computer programs in various programming languages.
3D models: Creating virtual objects for use in games, simulations, or design.

How does it work?

Generative AI models are trained on vast amounts of existing data. By analyzing this data, they learn the underlying patterns and structures, and then use this knowledge to generate new, similar data.

Common techniques used in generative AI include:

Generative Adversarial Networks (GANs): These use two neural networks, a generator and a discriminator, that compete against each other to produce increasingly realistic outputs.
Variational Autoencoders (VAEs): These learn a compressed representation of the input data and then use it to generate new data points.
Transformer networks: These are particularly effective at processing sequential data like text and have led to significant advances in natural language processing.

Examples of Generative AI in action:

ChatGPT: A conversational AI that can engage in natural-sounding dialogues, answer questions, and generate various creative text formats.
DALL-E 2 and Midjourney: AI art generators that can create images from textual descriptions.
GitHub Copilot: An AI pair programmer that can help write code.

Potential benefits of Generative AI:

Increased creativity and productivity: It can assist with creative tasks and automate repetitive ones.
Personalized experiences: It can generate content tailored to individual preferences.
New forms of art and entertainment: It can create entirely new forms of media and artistic expression.
Accelerated scientific discovery: It can help analyze complex data and generate new hypotheses.

Generative AI Online Training : Build AI Content Creation Skills (Highlights practical application)

Generative AI Online Training Course Content

OPERATORS
MATH LIBRARY
VARIABLES
DATA TYPES
TYPECASTING
BOOLEANS
STRINGS
SPECIAL CHARACTERS IN A STRING
SPIT & STRIP A STRING
LISTS
DICTIONARY
SETS
TUPLES
IF, WHILE, FOR, etc.,
FUNCTIONS
LAMBDA FUNCTIONS

Necessity of Numpy over Python
Built in Data Structures
Creation & Metadata of Numpy Arrays
Broadcasting
Built-in Functions
Data types
Type casting
Matrix Multiplication
Change of Numpy Shape
Numpy Slicing
Boolean Indexing
Filter Data
Statistical Methods
Sort, Min & Max of Numpy Arrays
Stacking & Splitting
Copy Vs View
Creation & Metadata of Numpy Arrays
Broadcasting
Built-in Functions
Data types
Type casting
Matrix Multiplication
Copy Vs View

Series
DataFrame
Metadata
Rename Columns & Indices
Transpose DataFrame
Slice a DataFrame
Boolean Indexing
Missing Values
Replace Values
Search, Extract & Create Columns
Set & Unset Index
Built-In Customised Functions
value_counts function
Groupby & Associated Methods
Concat & Append
Merge
Reshape – Stack & Unstack
Pivoting
Melt
Dummy Variables
Crosstab() method
Upper, Extract, Split & Replace Methods
Regular Expressions
Contains Method
StartsWith Method
Multiple String methods
Column Name Manipulation
Show Columns based on Keyword
read_csv() method
Bad Data
Select Columns based on DataType
Tabbed File
Fixed Width File
JSON Data
HTML Data
XML Data
API
Export Dataframe to CSV
Encoded Data Files

One Axis Plot
Two Axis Plot
Line Style & Color
X and Y Limits
Line Width
Multiple Plots in One Chart
Title, X & Y Labels
Gridlines
Annotations
Ticks
Spines
Legend
Subplots
Line Plot
Bar Graph
Scatter Plot
Area Plot
Box Plot
Histogram
Pie Chart

Count Plot
Box Plot
Violin Plot
Swarm Plot
Overlaying Plot of Univariate Variables
Facet Grid
Lmplot & regplot
Size & Shape of a Plot
Pair Plot
Join Plot
Heat Map

Types of Data
Population Vs Sample
Sampling Methods
Branches of Statistics
Distribution
Variance Vs. Standard Deviation
Z-Score
Correlation
Models
Probability
Types of Variables
Central Tendency
Measures of Dispersion
Statistics Library
Sampling Techniques
Covariance
Central Limit Theorem
Confidence Interval
Hypothesis Testing
Statistical Significance

Algebra Basics
Calculus
Matrices

Labelled Vs Unlabeled Data
Types of ML Algorithms
How ML Predict things
Count Vectorizer
Difference between fit and
fit_transform Methods
Special & Numerical Chracters
Remove HTML Tags from Text
Data
Remove Stop words from Text
Stemming
Train Test Split
Accuracy – MAE, MSE, RMSE
& Variance Score
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Classification Algorithms
Clustering Algorithms
Univariate Timeseries Analysis
Loss Functions
Noise
Penalty
Dimensionality Reduction
Principal Component Analysis
r^2 score
ROC & AUROC
HyperParameters
Project Skeletons
Average Ensemble Method
Weighted Ensemble Method
Conditional Ensemble Method
Bagging Ensemble Method
Boosting Ensemble Method
ML Template Regression
ML Template Classification
Principal Component Analysis
Eigen Values
Eigen Vectors

Artificial Neural Network
Deep Neural Network
Weight
Bias
Neuron
Hidden Layers
Input Layers
Output Layer
RNN
LSTM
CNN
Practical Projects, etc.,

An introduction to its core concepts and fundamental principles.

Basic NLP tasks

Frequency-based: Bag of Words, TF-IDF, N-grams
Distribution Models: CBOW, Skipgram, Word2Vec, GloVe

Autoencoders
Variational Autoencoders (VAE)
Applications of VAEs
Generative Adversarial Networks (GANs)
Applications and Types of GANs

Language Models
Types and Applications
Transformer Architecture
Key Transformer Models
BERT, RoBERTa, GPT Variants
Applications of Transformer Models

Introduction to Prompt Engineering
Principles of Prompt Engineering
Prompt Engineering Techniques
Crafting Effective Prompts
Priming Prompts
Prompt Decomposition

Generative AI Lifecycle
Reinforcement Learning with Human Feedback (RLHF)
LLM Pre-training and Scaling
Fine-tuning Techniques
Word Embeddings
Word2Vec, GloVe, FastText
Contextual Embeddings
ELMo, BERT, GPT
Sentence Embeddings
Doc2Vec, Infersent, Universal Sentence Encoder
Subword Embeddings
Byte Pair Encoding (BPE), SentencePiece
Use Cases of Embeddings

Chunking and Chunk Metrics
Introduction to Chunking
Traditional Chunking Techniques and Limitations
Advanced Chunking Techniques
Character Splitting
Recursive Character Splitting
Document-Based Chunking
Semantic Chunking
Agentic Chunking

Introduction to RAG
Components and Architecture of RAG
Building RAG with External Data Sources
Advanced RAG Techniques

Introduction to LangChain
Core Concepts and Components
Using LangChain Agents

Introduction to Vector Databases
Comparison with Traditional Databases
Types of Vector Databases
Open Source: ChromaDB, Weaviate, Faiss, Qdrant
Closed Source: Pinecone, ArangoDB, Cloud-Based Solutions
Text-Based LLM Evaluation
Automatic Metrics: BLEU, ROUGE, METEOR, BERT Score
Human Evaluation: Coherence, Factuality, Originality
Image-Based LLM Evaluation
Metrics: FID, IS, Perceptual Quality, Diversity Metrics
Audio-Based LLM Evaluation
Metrics: FAD, IS, PAQM Variants
Video-Based LLM Evaluation
Metrics: FVD, IS, Motion-Based, Content-Specific

Model Deployment and Management
calability and Performance Optimization
Security and Privacy
Monitoring and Logging
Cost Optimization
Model Interpretability

Amazon Bedrock
Azure OpenAI
Amazon SageMaker

ChatGPT, Gemini, Copilot
Claude
GROK API
Perplexity AI
DALL-E
MidJourney
Firefly

Deployment
Local Repo (Git)
AWS Bedrock
AWS SageMaker
AWS EKS
AWS ECR
Docker
Amazon DynamoDB
AWS S3
AWS EC2

Mendix Online training Training

Azure CI/CD
Azure AKS
Azure ACR
Azure SQL
Azure Cosmos DB
Azure Blob Storage

Generative AI Online Training Demo Videos

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Generative AI Online Training Common Faqs

What basic skills are helpful for getting started with Generative AI?

Basic computer literacy, familiarity with programming concepts (even at a high level), and a grasp of fundamental mathematical concepts (like probability and statistics) are beneficial. However, many tools offer user-friendly interfaces that require minimal coding. We provide Generative AI Online Training

Do I need to be a programmer to use Generative AI tools?

Not necessarily. Many user-friendly platforms and applications provide access to generative AI models without requiring extensive programming knowledge. However, programming skills are essential for developing new models, fine-tuning existing ones, and integrating them into complex systems.

What programming languages are most commonly used in Generative AI?

Python is the most popular language due to its rich ecosystem of libraries like TensorFlow and PyTorch, which are widely used for deep learning.

What are some key concepts I should learn to understand Generative AI better?

Key concepts include machine learning, deep learning, neural networks (especially recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers), training data, algorithms, and model evaluation metrics.

What educational qualifications are needed for a career in Generative AI?

A bachelor’s degree in computer science, data science, mathematics, statistics, or a related field is a good starting point. A master’s or Ph.D. in a specialized area like machine learning or artificial intelligence is often preferred for research-oriented roles. We provide Generative AI Online Training

What are some potential career paths in Generative AI?

Potential career paths include machine learning engineer, AI researcher, data scientist, deep learning specialist, prompt engineer, AI artist, and AI product manager.

What is a “prompt engineer,” and why is it becoming important?

A prompt engineer is someone skilled at crafting effective prompts (inputs) for generative AI models to elicit desired outputs. As these models become more sophisticated, the ability to create precise and creative prompts is increasingly valuable.

What is the job outlook for Generative AI-related roles?

The job outlook is very promising, as the demand for professionals with expertise in Generative AI is rapidly increasing across various industries.

What exactly is generative AI? How is it different from other types of AI?

Generative AI is a type of AI that can create new content, ranging from text and images to audio, code, and even 3D models. It differs from other AI that primarily focuses on tasks like classification or prediction.

How does generative AI work? What are the underlying technologies?

It often uses deep learning models, particularly neural networks, trained on vast amounts of data. These models learn the patterns and structures within the data and then use that knowledge to generate new, similar content.

What are some examples of generative AI in action?

Examples include text generation (like me!), image generation (DALL-E, Midjourney), music composition, code generation, and even drug discovery.

Can generative AI think or understand like humans?

No. While it can produce impressive results, it doesn’t possess consciousness, sentience, or true understanding. It works by recognizing patterns and probabilities, not by having genuine comprehension. We provide Generative AI Online Training

Is generative AI always accurate or reliable?

No. It can sometimes produce incorrect, nonsensical, or biased outputs, especially if the training data is flawed or if the prompts are ambiguous.

What are the limitations of generative AI?

Limitations include the need for large amounts of data, potential biases in the output, difficulty in controlling the creative process, and the risk of misuse.

What are the ethical concerns surrounding generative AI?

Concerns include the spread of misinformation, copyright infringement, job displacement, and the potential for malicious use. We provide Generative AI Online Training

How can we ensure responsible use of generative AI?

This involves developing ethical guidelines, promoting transparency, addressing biases, and fostering public awareness.

Will generative AI take my job?

While it may automate certain tasks, it’s more likely to augment human capabilities and create new job opportunities.

How can businesses use generative AI?

Businesses can use it for various purposes, such as content creation, marketing, product design, customer service, and research and development.

What industries are most impacted by generative AI?

Industries like media and entertainment, marketing, advertising, design, software development, and healthcare are significantly impacted. We provide Generative AI Online Training

How can I learn more about generative AI?

There are numerous online resources, courses, and tutorials available, as well as books and research papers.

What tools and platforms are available for using generative AI?

Many tools and platforms exist, ranging from cloud-based APIs to open-source libraries and user-friendly applications.