Generative AI for Developers | Level 1

Be at the Forefront of Cutting-Edge Technology in Delivering Ultimate Solutions! Join Our Exclusive Workshop to Unleash Your Creativity with Generative AI!

Are you ready to dive into the world of cutting-edge technology? Join us for our exclusive workshop, "Generative AI for Developers | Level 1," where you'll embark on a transformative journey into the realm of artificial intelligence. Whether you're a seasoned developer, an AI enthusiast, or a data scientist eager to expand your horizons, this event is tailored just for you.

During this immersive workshop, participants will explore the fundamentals of Generative AI, delving deep into its methodologies and practical applications. Led by industry experts, you'll gain hands-on experience and invaluable insights into harnessing the power of Generative AI to revolutionize your projects.

Key Outcomes and Benefits:

• Gain proficiency in fundamental concepts and methodologies of Generative AI.
• Learn how to leverage Generative AI algorithms to solve complex problems and optimize processes.
• Acquire practical skills to implement Generative AI solutions in your own projects.
• Network with like-minded individuals and industry professionals, fostering collaboration and knowledge exchange.

Who Should Attend:

• Developers and software engineers interested in exploring Generative AI.
• AI enthusiasts and professionals eager to build intelligent and innovative solutions.
• Data scientists and machine learning practitioners seeking to enhance their skills with OpenAI models.

Prerequisites:

• Familiarity with programming concepts (Python recommended).
• Basic understanding of statistics, machine learning, and deep learning.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Jupyter Lab or Google Colaboratory notebooks.

Don't miss this opportunity to unlock your creative potential with Generative AI. Reserve your spot today and embark on a journey of innovation and discovery!  

Module 1: Building the foundation

Recap of DeepLearning

• DeepLearning Basics & Artificial Neural Network Overview
• Building the Vocabulary – Terms & Concepts
• Training the Neural Networks
• Key Types of Neural Networks – CNN, RNN, LSTM, GANs
• Lab(s): Working with Neural Networks

Deep Learning on Azure –

• Azure AI Framework
• Various ways to develop AI Applications on Azure
• Examples of each of the options
• Getting started with Azure AI
• Lab(s): Getting started with Azure OpenAI Studio

Evolution of Natural Language Processing

• Introduction to NLP
• Rule-Based Approaches: Keyword matching and grammar rules
• Statistical Methods: n-grams, probabilistic context-free grammars
• Machine Learning for tasks like part-of-speech tagging, named entity recognition
• Word embeddings: Word2Vec, GloVe for semantic relationships
• Attention Mechanisms: Machine translation, text summarization, sentiment analysis
• Transformers: Architecture, self-attention, pre-trained language models like BERT, GPT
• Lab(s): NLP use cases with key approaches

Module 2: Understanding Generative AI and LLMs

Getting started with Generative AI

• Understanding Generative AI
• Types of Generative Models – autoregressive models, variational autoencoders, and generative adversarial networks (GANs)
• Categorizing generative models based on learning algorithms: likelihood-based vs. likelihood-free
• Motivation for generative modeling compared to discriminative models
• Characteristics of generative models: density estimation, data simulation, representation learning
• Lab(s): Getting started with Generative AI Models

Large Language Models (LLMs)

• Introduction to LLMs
• Use cases and tasks of LLMs
• Architecture of LLMs
• Generative Models for Text: Introduce generative models for text generation in NLP, including approaches like language modeling with autoregressive models, variational autoencoders (VAEs), and transformers.
• Evolution of text generation techniques
• Understanding role of Vector Databases
• Prompting and Prompt engineering
• Lab(s): Prompt Engineering

Evaluating LLMs

• Evaluating LLMs Significance and impact of Evaluation on natural language understanding and generation tasks
• Various Evaluation Metrics used to assess the quality and performance of LLMs                                                                                                              
  • Perplexity,
  • BLEU,
  • ROUGE,
  • METEOR, and others commonly used in machine translation, summarization, and text generation tasks

      

• Human Evaluation in assessing LLMs
• Intrinsic & Extrinsic Evaluation
• Dataset Quality and Bias
• Interpretability and Explainability
• Robustness and Generalization
• Evaluation in Low-Resource and Multilingual Settings
• Fairness and Bias Evaluation

Fine Tuning Basics

• Background and concept
• Curse of dimensionality
• Graphical models (Bayesian networks)
• Comparison of generative and discriminative models
• Lab(s): Fine Tuning for specific tasks

Scaling Human Feedback

• Challenges and considerations in scaling human feedback
• Strategies for collecting and incorporating large-scale feedback

Module 3: Working with Azure OpenAI

Lab(s): Text Generation on Azure OpenAI

Module 4: Transformer Models and BERT Model

OpenAI’s GPT Models and BERT Model

• Understanding the architecture of BERT
• Introduction to OpenAI’s GPT models
• Generating text using GPT models
• Exploring image generation use cases
• Lab(s): Working with GPT Model

Module 5: AutoGPT Framework Examples and Use Cases

Introduction to AutoGPT

• Understanding how AutoGPT works
• Architecture and autonomous iterations
• Memory management and multi-functionality

GPT-4: Fully Autonomous Models

• Overview of GPT-4 and its unsupervised operation
• The future of generative agents

AutoGPT Use Cases

• Examples of using AutoGPT framework in various applications:                                                                                                              
  • Writing codes
  • Building an app
  • Ordering a pizza
  • Researching
  • Preparing podcasts
  • Improving Google Workspace
  • Philosophizing
  • Ethical considerations in AI

    DATES & TIMES: Oct 07, 2024, 9:00 AM GMT+8 – Oct 07, 2024, 5:00 PM GMT+8                                      Oct 08, 2024, 9:00 AM GMT+8 – Oct 08, 2024, 5:00 PM GMT+8                                      Oct 09, 2024, 9:00 AM GMT+8 – Oct 09, 2024, 5:00 PM GMT+8                                      Oct 10, 2024, 9:00 AM GMT+8 – Oct 10, 2024, 5:00 PM GMT+8                                      Oct 11, 2024, 9:00 AM GMT+8 – Oct 11, 2024, 5:00 PM GMT+8