Description
What is Generative AI?
Generative AI is a type of artificial intelligence designed to create new content by learning patterns from existing data. This can include generating text, images, audio, video, and even complex software code. Unlike traditional AI, which typically focuses on classifying or predicting based on given data, generative AI uses machine learning models—most often deep neural networks to generate new, original data that resembles the patterns in its training set.
Industries Hiring Generative AI Talent:Â
Generative AI talent is sought across a variety of industries, including:
Tech and Software Development: Companies building AI-powered products and platforms, from search engines to digital assistants.
Healthcare: For drug discovery, medical imaging, and personalized treatment plans.
Finance: To enhance fraud detection, algorithmic trading, and customer interactions.
Media and Entertainment: To generate media content, create CGI effects, or enhance video games with dynamic storytelling.
E-commerce: For product recommendations, customer service, and personalized marketing.
Marketing and Advertising: Using generative AI to create targeted ads, personalized emails, and customer segmentation.
Text Generation: Chatbots, writing assistance, code generation.
Image and Video Generation: Art creation, image editing, deepfake generation.
Music and Sound: Synthesizing new music or sound effects.
The transformative nature of Generative AI lies in its potential to create realistic, high-quality content that can assist, inspire, or automate a wide range of human activities, from creative tasks to highly technical applications.
Job Market and How much does an AI consultant make?
The job market for Generative AI is rapidly growing as companies across many sectors recognize its potential to automate tasks, create personalized content, and generate insights. The demand for generative AI talent has led to competitive salaries. According to recent estimates, salaries in the U.S. range from $100,000 to over $200,000 annually, with senior and specialized roles commanding higher compensation.
The following topics will be covered as part of Generative AI Course .Â
GENAI intro
Understanding Transformers: Learn the architecture and inner workings of transformers, the backbone of many generative AI models.
Understanding Terminologies of LLMs: Key concepts like parameters, attention mechanisms, pre-training, fine-tuning, zero-shot learning, etc.
LLM Leaderboard Comparison: A comparison of various LLMs based on performance benchmarks, use cases, and capabilities.
Current Applications of GENAI: Explore practical applications like text generation, code assistance, chatbots, content creation, and other AI-driven solutions.
Prompt Engineering
Basics & Best Practices: An introduction to prompt engineering, focusing on how to craft effective prompts for LLMs.
Different Techniques to Master It: Strategies like few-shot prompting, zero-shot prompting, chain-of-thought prompting, and other advanced methods.
Risk Mitigation Techniques: How to handle biases, avoid misleading outputs, and generate more accurate and reliable results.
Introduction to GPT and LLAMA LLMS
Overview of GPT Models:
- Explore the evolution of GPT (Generative Pre-trained Transformers) from GPT-1 to the latest versions, focusing on key innovations.
- Discuss the strengths of GPT, such as its general-purpose nature, language fluency, and ability to generate coherent text.
- Understand the limitations of GPT models, like biases and hallucinations.
Introduction to LLAMA Models:
- Overview of Meta’s LLAMA (Large Language Model Meta AI) and its purpose as an open, research-focused LLM.
- Compare LLAMA to other LLMs in terms of scale, training data, and performance on language tasks.
- Explore unique features of LLAMA, including architectural differences and efficiency in training smaller models with comparable capabilities.
Comparison of GPT and LLAMA:
- Highlight similarities and differences between GPT and LLAMA in terms of architecture, scale, performance, and training efficiency.
- Examine use cases for both models in various domains like content creation, code generation, customer service, and academic research.
Hands-on Exploration:
- Demonstration of using pre-trained GPT and LLAMA models with platforms like Hugging Face, API access, and prompt examples.
- Fine-tuning a small-scale LLM to understand the customization process for specific use cases.
Understanding Embeddings and Vector Databases
What are Embeddings?:
- Introduction to embeddings as dense numerical vectors representing words, sentences, or documents.
- Explore why embeddings are crucial for semantic understanding in LLMs, capturing the meaning and context of text data.
- Discuss the different types of embeddings: Word2Vec, GloVe, FastText, BERT, etc.
How Embeddings are Generated:
- Understanding the role of LLMs in generating embeddings and how the transformer architecture supports contextual embeddings.
- Explore dimensionality reduction techniques to visualize high-dimensional embedding spaces.
Applications of Embeddings:
- Semantic search: How embeddings power intelligent search systems by matching intent rather than exact words.
- Clustering and categorization: Group similar documents or products using embeddings.
- Recommendation systems: Use embeddings to suggest relevant items based on user preferences.
Introduction to Vector Databases:
- Explain what vector databases are and how they differ from traditional databases in storing and retrieving embeddings.
- Discuss the importance of vector similarity search for efficient retrieval of data using nearest neighbor algorithms.
Hands-on with Vector Databases:
- Introduction to popular vector databases like Pinecone, FAISS, and Milvus.
- Walkthrough on setting up a vector database, storing embeddings, and performing semantic search.
- Explore real-world examples such as building a Q&A system with embeddings and vector databases for retrieval.
Techniques for LLM Fine Tuning
PEFT (Parameter-Efficient Fine-Tuning): Focuses on tuning specific model parts to save resources.
LoRA (Low-Rank Adaptation): A method to fine-tune large models with reduced computational requirements.
Instruct Fine-Tuning: Tailoring models to follow specific instructions by training them on custom datasets.
LLM Application Development with langchain and llamaindex
Introduction to Langchain:
- Overview of Langchain’s purpose in creating pipelines for LLM applications.
- Understanding Langchain’s architecture: chains, prompts, tools, and memory.
- Exploring Langchain use cases, such as conversational agents, code analysis, and custom workflows.
Building Pipelines with Langchain:
- How to connect LLMs to data sources, tools, and APIs.
- Techniques to manage complex conversations using Langchain’s memory management.
- Hands-on example of building a chatbot using Langchain, integrating it with multiple APIs.
Introduction to LlamaIndex:
- Overview of LlamaIndex and its role in efficient data indexing for LLM applications.
- Understanding how LlamaIndex optimizes data retrieval and integrates with LLMs.
- Practical scenarios like document indexing, real-time data querying, and knowledge-based Q&A.
Hands-on with Langchain and LlamaIndex:
- Step-by-step guide to setting up a basic LLM-based application with Langchain.
- Creating and indexing documents with LlamaIndex for efficient search.
- Building a unified app that combines Langchain and LlamaIndex for data interaction and querying.
Generating Images - Stable Diffusion and DALL-E 3
Introduction to Generative AI for Images:
- Overview of how generative AI models create images from text prompts.
- Introduction to the concept of diffusion models and transformer-based image generation.
Stable Diffusion:
- Understanding the Stable Diffusion model, including architecture and workflow.
- Use cases of Stable Diffusion in generating high-quality, detailed images.
- Hands-on example of generating images from simple prompts using a Stable Diffusion tool.
DALL-E 3:
- Overview of DALL-E 3, a transformer-based model for creative image generation.
- Exploring unique features of DALL-E 3, such as compositional generation and style transfer.
- Practical examples of using DALL-E 3 for artistic and professional purposes.
Comparing Stable Diffusion and DALL-E 3:
- Key differences between Stable Diffusion and DALL-E 3, including strengths and limitations.
- Scenarios for choosing the right model for specific creative tasks.
Creating Realistic Videos from Text using OpenAI Sora
Introduction to Text-to-Video Generation:
- Overview of AI-generated videos, the technology behind them, and current limitations.
- Understanding OpenAI Sora and its capabilities in transforming text into realistic video content.
Creating Videos with OpenAI Sora:
- Step-by-step guide to generating videos using Sora, from text prompts to video output.
- Techniques to improve video quality, manage style, and adjust scene composition.
Applications of AI-Generated Videos:
- Practical examples of using AI-generated videos for marketing, storytelling, and education.
- Ethical considerations in creating and using AI-generated videos.
LLM Model Evaluation
Quantitative Evaluation Metrics:
- Key performance metrics like accuracy, perplexity, BLEU score, ROUGE, F1-score, etc.
- Understanding what each metric measures and its relevance to LLM evaluation.
Qualitative Evaluation Methods:
- Approaches to assess model output for fluency, coherence, creativity, and user experience.
- Techniques like human evaluation, crowdsourcing feedback, and scenario testing.
Bias and Fairness Evaluation:
- Tools and methods to evaluate and mitigate biases in LLMs.
- Understanding the sources of bias in training data and strategies to address them.
Hands-on LLM Evaluation:
- Practical guide to evaluating a fine-tuned LLM using both quantitative and qualitative methods.
- Setting up experiments to compare models and identify areas of improvement.
Responsible AI Practices
Understanding AI Ethics:
- Overview of ethical considerations in AI development, including privacy, transparency, and accountability.
- Discuss the social impact of LLMs and generative AI on society and culture.
Bias Detection and Mitigation:
- Techniques to identify and reduce biases in AI models.
- Implementing bias detection tools and best practices for inclusive model development.
Explainability and Transparency:
- Importance of AI explainability for trust and user acceptance.
- Tools and techniques to make LLMs more interpretable and transparent.
Legal and Regulatory Considerations:
- Overview of regulations and standards related to AI, such as GDPR, AI ethics guidelines, and industry-specific rules.
- Best practices for compliance with AI ethics and responsible usage in commercial settings.
Building a Responsible AI Framework:
- Step-by-step guide to establishing a responsible AI framework for organizations.
- Implementing monitoring, auditing, and feedback loops to ensure ethical AI deployment.
Prerequisites :
Basic of any programming knowledge but Python is preferred.
The student should have good logical and reasoning skills.
Duration & Timings :
Duration – 60 Hours.
Training Type: Instructor Led Live Interactive Sessions.
Faculty: Experienced.
Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 10 Weeks. January 11, 2025. ( Started )
Weekday Session – Mon – Thu 8:30 PM to 10:30 PM (EST) – 8 Weeks. February 3, 2025.
Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 10 Weeks. March 15, 2025.
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