Generative Ai Training in Hyderabad

Basics of Python Programming
Installation and Setup:
● Installing Python and setting up a development environment (IDEs
like PyCharm, VSCode, Jupyter Notebooks)
Syntax and Basic Constructs:
● Variables and data types (integers, floats, strings, booleans)
● Basic input and output
● Comments and documentation
Control Structures
 Conditional Statements:
● if, elif, else
 Loops:
● for, while
● Loop control statements (break, continue, pass)
Functions
Defining Functions:
● Parameters and return values
Scope and Lifetime:
● Local and global variables
Lambda Functions:
● Anonymous functions

● Introduction to Generative AI
● AI vs ML vs DL vs NLP vs Generative AI
● Generative AI principles
● What is the role of ML in Gen-AI
● Different ML techniques (Supervised, Unsupervised, Semisupervised & Reinforcement Learning)
● Applications in various domains
● Ethical considerations

● NLP essentials
● Basic NLP tasks
● Different text classification approaches
● Frequency based – Bag of words,TF-IDF, N-gram.
● Distribution Models – CBOW, Skipgram(Traditional approaches)and
word2vec, Glove.
● Ensemble Methods (Random Forest, Gradient Boosting, AdaBoost) &
Traditional Machine Learning Models – Naïve Bayes, Support Vector
Machine (SVM), Decision Trees, Logistic Regression.
● Deep learning techniques – CNNs, RNNs, LSTMs, GRU and
Transformers.

● Autoencodes
● VAE’s and applications
● GAN’s and it’s applications
● Different types of GAN’s and applications

● Different types of Language models
● Applications of Language models
● Transformers and its architecture
● BERT, RoBERTa, GPT variations
● Applications of transformer models

● What is Prompt Engineering
● What are the different principles of Prompt Engineering
● Types of Different Prompt Engineering Techniques
● How to Craft effective prompts to the LLMs
● Priming Prompt
● Prompt Decomposition

● Generative AI lifecycle
● What is RLHF
● LLM pre-training and scaling
● Different Fine-Tuning techniques

● What are word embeddings
● What is the use of word embeddings, where we can use it?
● Word Embeddings – Word2Vec, GloVe and FastText
● Contextual Embeddings – ELMo , BERT and GPT
● Sentence Embeddings – Doc2Vec, Infersent, Universal Sentence
Encoder
● Subword Embeddings – BPE(Byte Pair Encoding), Sentence Piece
● Usecase of Embeddings.

● What is Chunking
● What is the use of chunking the document
● What are the traditional effective chunking techniques
● What are the problems and limitations with traditional chunking
techniques?
● How to overcome the limitations of Traditional chunking
● Advanced Chunking Techniques:
1. Character Splitting
2. Recursive Character Splitting
3. Document based Chunking
4. Semantic Chunking
5. Agentic Chunking

● What is RAG
● What are the main components of RAG
● High level architecture of RAG
● How to Build RAG using external data sources
● Advanced RAG

● What is Langchain
● What are the core concepts of Langchain
● Components of Langchain
● How to use Langchain agents

● LlamaIndex
● What are Vector Databases
● Why do we prefer Vector Databases over Traditional Databases
● Different Types of Vector Databases: OpenSource and Close Source
● OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
● Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based
Solutions

● Supervised Finetuning
● Repurposing-Feature Extraction
● Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA

• Text based LLMs
  Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
  Score.
  Human Evaluation: Coherence, Factuality, Originality, Engagement
• Image based LLMs
  Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
  Distance), IS (Inception Score), Perceptual Quality Metrics,
  Diversity Metrics.
  Human Evaluation: Photorealism, Style, Creativity, Cohesiveness
• Audio generation LLMs
  Automatic Evaluation:  
• FAD (Frechet Audio Distance), 
• IS (Inception Score), 
• Perceptual Quality Metrics – PAQM, 
•  PAQM – SNR (Signal-to-Noise Ratio), 
• PAQM – PESQ (Perceptual Evaluation of Speech Quality)
  Human Evaluation: 
• Perceptual Quality – PQ, 
•  PQ- Naturalness,
•  PQ-Fidelity, 
• PQ- Musicality, 
• Task Specific Evaluation.
• Video Generation LLMs
  Automatic Evaluation: FVD (Frechet Video Distance), Inception
  Score(IS), Perceptual Quality Metrics, Motion Based Metrics –
  Optical Flow Error, Content-Specific Metrics.
  Human Evaluation: Visual Quality, Temporal Coherence, Content
  Fidelit.

•  Model Deployment and Management
•  Scalability and Performance Optimization
•  Security and Privacy
•  Monitoring and Logging
• Cost Optimization
• Model Interpretability and Explainability.

● Amazon Bedrock, Azure OpenAI

● ChatGPT, Gemini, Copilot