GenAI and It's Industry Applications
- 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, Semi-supervised & Reinforcement Learning).
- Applications in various domains.
- Ethical considerations.
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NLP & Deep Learning
- 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.
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Generative AI Models
- Autoencoders.
- VAE’s and applications.
- GANs and it’s applications.
- Different types of GANs and applications.
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Language Models & Transformer Models
- Different types of Language models
- Applications of Language models
- Transformers and its architecture
- BERT, RoBERTa, GPT variations
- Applications of transformer models
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Prompt Engineering
- 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
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Large Language Models
- Generative AI lifecycle
- What is RLHF
- LLM pre-training and scaling
- Different Fine-Tuning techniques
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LLM's Embeddings
- 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.
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Different Chunk Metrics
- 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
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RAG and Advanced RA with Langchain
- 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
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Langchain for LLMs
- What is Langchain
- What are the core concepts of Langchain
- Components of Langchain
- How to use Langchain agents
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Vector Databases
- 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
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Finetuning LLMs
- Supervised Finetuning
- Repurposing-Feature Extraction
- Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA
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LLMs Evaluation
- 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, PQFidelity, 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.
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LLMops
- Model Deployment and Management
- Scalability and Performance Optimization
- Security and Privacy
- Monitoring and Logging
- Cost Optimization
- Model Interpretability and Explainability.
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LLM's on Cloud
- Amazon Bedrock, Azure OpenAI
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Different AI Tools
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Corporate Training
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