Generative AI
• Introduction to AI
• AI vs ML vs DL vs NLP vs Generative AI
• Generative AI principles
• Role of ML in Generative AI
• Different ML techniques (Supervised, Unsupervised, Semi-supervised, Reinforcement Learning)
• Applications in various domains
• Ethical considerations
NLP and Deep Learning
• NLP essentials
• Basic NLP tasks
• Different text classification approaches
• Frequency based: TF-IDF, BoW, N-gram
• Distribution Models: CBOW, Skipgram (Traditional approaches), Word2Vec, GloVe
• Ensemble and traditional Machine learning Models: Naive Bayes, SVM, Logistic Regression, Decision Trees
• Deep learning techniques: CNNs, RNNs, LSTMs, GRU, Transformers
Generative AI models
• Autoencoders
• VAE's and applications
• GAN's and its applications
• Different types of GAN's and applications
• Language Models and Transformer models
• Different types of Language models
• Applications of Language models
• Transformers and its architecture
• BERT, RoBERTa, GPT variations
• Applications of transformer models and Hugging face
Prompt Engineering
• What is Prompt Engineering
• Different principles of Prompt Engineering
• Types of Different Prompt Engineering techniques
• Crafting effective prompts to LLMs
• Priming Prompt
• Prompt Decomposition
Generative AI Lifecycle and LLMs working procedure
• Generative AI lifecycle
• What is RLHF
• LLM pre-training and scaling
• Different fine-tuning techniques
Different Embeddings and Techniques
• Word embeddings
• Use cases of word embeddings
• Word Embeddings: Word2Vec, GloVe, FastText
• Contextual Embeddings: ELMo, BERT, GPT
• Sentence Embeddings: Doc2Vec, Infersent, Universal Sentence Encoder
• Subword Embeddings: BPE (Byte Pair Encoding), Sentence Piece
• Use case of Embeddings
Different chunk metrics
• Chunking
• Use of chunking the document
• Traditional effective chunking techniques
• Problems and limitations with traditional chunking techniques
• Overcoming limitations of Traditional chunking
• Advanced chunking techniques:
• Character splitting
• Recursive character splitting
• Document based chunking
• Semantic Chunking
• Agentic Chunking
RAG and Advanced RA with Langchain
• What is RAG
• Main components of RAG
• High-level architecture of RAG
• Building RAG using external data sources
• Advanced RAG
Langchain for LLMs
• What is Langchain
• Core concepts of Langchain
• Components of Langchain
• How to use Langchain agents
Vector Databases
• LlamaIndex
• Vector Databases
• Why prefer Vector databases over traditional databases
• Different types of Vector databases: Open-source and Close Source
• Open-source: Chroma DB, Weaviate, Faiss, Qdrant
• Close-Source Vector Databases: Pinecone, ArangoDB, Cloud-Based Solutions
Finetuning LLMs
• Supervised Finetuning
• Repurposing-Feature Extraction
• Advanced techniques in Supervised finetuning: PEFT, LoRA, QLoRA
LLM's on Cloud
• Amazon bedrock, Azure OpenAI, GCP
LLMs Evaluation
• Text based LLMs:
• Automatic Evaluation: BLUE 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 Audio 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: Fréchet Video Distance (FVD), Inception Score (IS), Perceptual Quality Metrics, Motion-Based Metrics - Optical Flow Error, Content-Specific Metrics
• Human Evaluation: Visual Quality, Temporal Coherence, Content Fidelit
LLMops
• Model Deployment and Management
• Scalability and Performance Optimization
• Security and Privacy
• Monitoring and Logging
• Cost Optimization
• Model Interpretability and Explainability
• Continuous Integration and Deployment (CI/CD)
• Collaboration and Workflow Management
• Regulatory Compliance
• Disaster Recovery and Failover
Different AI Tools
• ChatGPT, Gemini, Copilot, Claude, Perplexity.
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