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Boosting Developer Productivity with AI

In this Boosting Developer Productivity with AI course, participants learn how to use artificial intelligence (AI) to improve their efficiency, creativity, and problem-solving across diverse domains....

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$1,525 USD
Course Code WA3417
Duration 2 days
Available Formats Classroom, Virtual

In this Boosting Developer Productivity with AI course, participants learn how to use artificial intelligence (AI) to improve their efficiency, creativity, and problem-solving across diverse domains.

Skills Gained

  • Understand LLMs' fundamental concepts and principles
  • Gain insights into the diverse applications of LLMs across various domains, including natural language processing, creative text generation, and code development
  • Enhance productivity and problem-solving with AI
  • Develop proficiency using popular LLM platforms and tools like OpenAI's ChatGPT and GitHub Copilot
  • Explore ethical considerations and potential risks associated with LLM usage
  • Apply LLM-powered techniques to practical scenarios

Who Can Benefit

  • Audience
  • Software developers
  • IT architects
  • Technical managers

Prerequisites

Students should have an IT background or be interested in generative AI-driven programming.

Course Details

Outline

Chapter 1 - Introduction to Large Language Models

  • What is Generative AI?
  • A Bit of History ...
  • ... and Then ...
  • RNNs
  • Problems with RNNs
  • Transformers
  • Encoders and Decoders
  • Generative AI and LLMs
  • Training the Model to Predict the Next Word Visually
  • The LLMs Landscape
  • The Evolutionary Tree of LLMs
  • The Microsoft 365 Copilot Ecosystem
  • The LLM Capabilities vs LLM Size (in Parameters)
  • Does the Model Size Matter?
  • Inference Accuracy vs LLM Size
  • Open AI GPT Models
  • Llama
  • The LLaMA Family of LLMs
  • LLaMA 2
  • The AI-Powered Chatbots
  • How Can I Access LLMs?
  • Options for Accessing LLMs
  • Cloud Hosting
  • Opinions about LLMs
  • Multimodality of LLMs
  • Infographic of Multimodality Tasks
  • Example of an LLM Explaining a Joke
  • Example of Cause & Effect Reasoning
  • Inferring Movie from Emoji
  • Prompt Engineering
  • The Right People, with the Right Skills, for the Right Time ...
  • Context Window and Prompts
  • Zero- and Few-Shot Prompting
  • The Training Datasets
  • The RedPajama Project (OSS LLaMA Dataset)
  • AI Alignment
  • Reinforcement Learning with Human Feedback (RLHF)
  • Problems with RLHF
  • Ethical AI
  • Summary

Chapter 2 - LLMs, a Technologist's Perspective

  • LLM Operational Aspects
  • Understanding Model Sizes
  • Physical Model Sizes
  • The Training and Inference Costs
  • The Model Training Phase's Carbon Footprint
  • Quantization
  • Model Formats
  • LLM Accuracy Benchmarks
  • Open and Closed Book Benchmarks
  • The Perplexity Performance Metric
  • Embeddings
  • Where are Embeddings Used?
  • The Vector Databases
  • LLM Concerns
  • Ways to Interface with Local LLMs
  • Using a Supported Programming API (Binding)
  • UI Options
  • Customization Options for LLMs
  • Customization Options: Top-p and Top-k
  • Customization Options: Temperature and Repetition Penalty
  • Customization Option: The Turn Template
  • Configuration Presets
  • Summary

Chapter 3 - Introduction to ChatGPT

  • A Stylized OpenAI ChatGPT Logo
  • OpenAI GPT Models
  • OpenAI Models
  • ChatGPT 4.0
  • ChatGPT Prompts
  • ChatGPT Prompts Strategies, Tactics, and Best Practices
  • Prompt Engineering: Dealing with ChatGPT's Hallucination Syndrome
  • Prompt Engineering: Break Down the Complex Tasks into Smaller Ones
  • Prompt Engineering: Examples of Prompts
  • OpenAI API
  • GPT Embeddings
  • Embedding Models' Risks and Limitations
  • OK. How Can I Get My OpenAI Embedding
  • Tokens, Take 1
  • Tokens, Take 2
  • The Tokenizer UI
  • Prompts, Embeddings, and Tokens
  • Summary

Chapter 4 - AI-Powered Developer Productivity

  • Generative AI and LLMs for Developers
  • How to Become a Technologies and Philosopher All in One
  • Gartner on AI-augmented Development Tools
  • Developer-AI Pair Programming Paradigm
  • The Tooling
  • Some Facts ...
  • Code Generation: SQL Example
  • Code Generation: Using ThreadLocal Storage in Java
  • Code Generation: Thread-safe Singleton Design Pattern in C#
  • Code Generation: Bash Scripting
  • Code-to-Code Translation
  • Code Llama
  • Fine-Tuning Llama 2 Workflows
  • GitHub Copilot
  • Can I Trust AI-Generated Code?
  • The Safeguards
  • The General Recommendations ...
  • Summary

Chapter 5 - Introduction to GitHub Copilot

  • What is GitHub Copilot?
  • Copilot Chat
  • IDE and REPL Integrations
  • Will Copilot Replace Developers?
  • Can I Trust Code Generated by GitHub Copilot Code?
  • GitHub Copilot's Modus Operandi
  • The Life of a Code Completion: The Big Picture
  • Code Suggestions are Not Copy & Paste from Other Peoples' Code
  • The Shebang Prologue Hint
  • Getting Started with GitHub Copilot
  • GitHub Copilot Plans
  • Copilot for Individuals
  • Copilot for Businesses
  • GitHub Copilot Security
  • Responsible Copilot
  • Summary

Lab Exercises

  • Lab 1. Learning the Colab Jupyter Notebook Environment
  • Lab 2. Hello, AI!
  • Lab 3. OpenAI Platform Overview
  • Lab 4. Using OpenAI API
  • Lab 5. Understanding Embeddings
  • Lab 6. OpenAI API Project
  • Lab 7. Copilot Environment Setup
  • Lab 8. Hello, Copilot!
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