Here's an overview of the program content:
Assignment doc link
- What is Gen AI?
- Evolution of Gen AI
- How does Gen AI work?
- Types of Gen AI
- Examples of Gen AI in use today
- Benefits and challenges of Gen AI
- What is the Foundation Model?
- Types of Foundation Models (table with 5+ parameters)
- How do large language models work?
- What is GPU and their role in AI & ML
- Capabilities and limitations of large language models
- How are large language models used in Gen AI?
- Training a foundation model
- Customizing a foundation model
Hidevs Community: Harnessing the Power of Gen AI for Business Transformation
The Fundamentals of Gen AI & Foundation Models
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Open source
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Closed source
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Popular models
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Multimodels
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Models based on use case
- Zero shot
- Few shot
- One shot
- Chain of thoughts
- Tree of thought
Chatgpt prompt engineering for developers short courses by Andrew Ng
- Basic Python
- Object-Oriented Programming (OOP) concepts
- Python data structures
- File reading
- Add contact details
- Delete contact details
- Update contact details
- Search contact details by name
- Virtual environment setup
- Exploring offline and online data science IDEs
- Introduction to machine learning libraries like Pandas, NumPy, and Matplotlib
- Setup data science environment
- Data manipulation or preprocessing with Pandas
- Descriptive analysis and matrix calculation with NumPy
- Data visualization with Matplotlib
- Machine learning libraries like Pandas, NumPy, and Matplotlib
- Feature engineering and model training with Scikit-learn library
- Regression algorithms like linear regression and decision tree
- Evaluation metrics for regression algorithms like R2 Score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
- Data manipulation or preprocessing with Pandas
- Descriptive analysis and matrix calculation with NumPy
- Data visualization with Matplotlib and Seaborn
- Feature engineering with Scikit-learn
- Regression algorithms model training with Scikit-learn and Pickle
- Evaluation of machine learning model
- Introduction to NLP
- Getting Started with NLTK (Natural Language Toolkit)
- NLP basic terminologies and their extraction
- Approaching NLP problems
- Text Pre-processing
- Feature Engineering (Featurization) of text data
- Text Classification – Classifying the news category
- Model Deployment
- ETL (Extract, Transform, Load)
- Querying
- Pipelines
- Smart contracts
- Decentralized applications (DApps)
- Consensus mechanisms
- Tokenization
- Interoperability
- Privacy and security protocols
- Governance models
- Cross-chain communication
- 5W1H on RAG
- Basic Data Cleaning
- Vector Database
- Knowledge Graph Database
- Chunking
- Embedding
- LLM Models
- Trulens
- Q&A on PDF/CSV/DOC
- 5W1H on FT (Who, What, When, Where, Why, How)
- PFET
- LoRA
- QLoRA
- Prefix tuning
- Prompt tuning
- Fine Tuning by multiple methods
- Front end: Streamlit (React or Angular)
- Back end: Langchain
- Database: Chroma or FAISS (for Vector DB)
- Open Source
- Deployment: Hugging Face Spaces
- Trulens
- Hallucination
- Latency
- Costing
- Ethical considerations
- Data privacy and security concerns
- Bias in Gen AI models
- How to mitigate the challenges of Gen AI
- Popular Gen AI development tools
- How to choose the right tools for your project
- Coding tools
- Product tools
- HR tools
- Common Tools
- Find use cases
- Expense sheet
- Revenue sheet
- Profit margin
- Top 4 Pricing API models
- Top 4 Open Source API models
- Benefits and drawbacks of pricing vs open source API models
- The cost of developing and deploying Gen AI
- Factors that affect the cost of Gen AI
- How to reduce the cost of Gen AI
- How companies are using Gen AI to improve their businesses
- Examples of successful Gen AI integrations
- Resume Building
- Linkedin Profile Building
- Job Apply 50+ in single day at $0