Generative AI Introduction and Overview E-Learning Training 

Een praktische introductie tot de wereld van Generative AI – van theorie tot eigen model.

Deze cursus biedt een solide introductie tot Generative AI, met als doel je niet alleen vertrouwd te maken met de basisprincipes, maar ook te leren hoe je ze in de praktijk toepast. Je begint bij de fundamenten zoals toepassingen, kernbegrippen en deep learning-technieken, en bouwt vervolgens voort naar geavanceerde methodologieën en ethische vraagstukken.

De leerreis sluit af met een praktisch project waarin je zelf een Generative AI-model bouwt. Daarmee vertaal je theorie direct naar praktijk en krijg je grip op hoe je Generative AI kunt inzetten binnen jouw vakgebied of organisatie.

Waarom kiezen voor deze opleiding?

• Verken de fundamenten en toepassingen van Generative AI
• Leer de werking van deep learning binnen AI-context
• Bouw je eigen Generative AI-model als afsluitend project
• Begrijp ethische uitdagingen en hoe AI verantwoord te gebruiken
• Inclusief 365 dagen toegang tot alle leerpaden, labs, mentor en e-learning

Wie zou moeten deelnemen?

Deze training is geschikt voor:

• Data scientists en AI-engineers die hun kennis willen verbreden
• IT-managers en technische projectleiders
• Nieuwkomers in AI met interesse in praktijkgerichte introductie
• Professionals wereldwijd die AI in hun organisatie willen toepassen

Deze Learning Kit met meer dan 11 leeruren is verdeeld in drie sporen:

Demo Generative AI Introduction and Overview Training

Cursusinhoud

Track 1: Generative AI Overview

In this track, the focus will be on applications, key concepts, and deep learning techniques of Generative AI, progressing towards more complex topics like advanced methodologies and ethical issues. The series ends in a practical project where learners can apply their acquired knowledge to build a Generative AI model, providing a hands-on experience that reinforces theoretical learning.

An Introduction to Generative AI

Course: 1 Hour, 41 Minutes

• Course Overview
• What Is Generative AI?
• Content Generation Using Generative Models
• Prompt Engineering
• Evolution of Generative AI
• Recent Advances in Generative AI
• Applications of Generative AI
• Generative AI Risks
• Ethical Considerations in Working with Generative AI
• Deepfake AI
• Course Summary

Generative AI Models: Getting Started with Autoencoders

Course: 2 Hours, 15 Minutes

• Course Overview
• Autoencoders for Representation Learning
• Autoencoder Architectures
• Setting up the Google Colab Environment
• Downloading and Exploring the Fashion MNIST Dataset
• Configuring the Autoencoder Architecture
• Training the Autoencoder and Visualizing Reconstructions
• Training Convolutional Autoencoders
• Reconstructing Multichannel Images with DNN Autoencoders
• Reconstructing Multichannel Images with CNN Autoencoders
• The Denoising Autoencoder
• Configuring and Training Denoising Autoencoders
• The Sparse Autoencoder
• Course Summary

Generative AI Models: Generating Data Using Variational Autoencoders

Course: 1 Hour, 18 Minutes

• Course Overview
• Variational Autoencoders
• Variational Autoencoder Implementation
• Setting up the Variational Autoencoder
• Training a Variational Autoencoder
• Training a VAE Using Convolutional Networks
• Training VAEs on Multichannel Images
• Course Summary

Generative AI Models: Generating Data Using Generative Adversarial Networks

Course: 1 Hour, 40 Minutes

• Course Overview
• Generative Adversarial Networks (GANs)
• Generative Adversarial Network Training
• Setting up a Virtual Environment for GAN Training
• Loading and Exploring the MNIST Dataset
• Creating a Generator and a Discriminator
• Training a Generative Adversarial Network
• Deep Convolutional Generative Adversarial Networks (DCGANs)
• Loading and Exploring the CelebFaces Dataset
• Training a DCGAN
• Course Summary

Using OpenAI APIs: Exploring APIs with the OpenAI Playground

Course: 1 Hour, 45 Minutes

• Course Overview
• Introduction to Generative AI and Large Language Models (LLMs)
• ChatGPT and the OpenAI Playground
• OpenAI Application Programming Interfaces (APIs)
• Exploring OpenAI APIs and Pricing
• Getting Started with the OpenAI Playground
• Assigning Personas and Tweaking Creativity
• Using the Legacy Completions API via the Playground
• Exploring OpenAI Prompt Examples
• Creating an OpenAI Assistant
• Querying the OpenAI Assistant
• Course Summary

Using OpenAI APIs: Accessing OpenAI APIs from Python

Course: 1 Hour, 53 Minutes

• Course Overview
• Generating Application Programming Interface (API) Keys to Use with Endpoints
• Making API Requests with cURL
• Making Our First API Request Using Python
• Exploring Chat Completions and JSON Mode
• Streaming Chat Completions with the Legacy Completions API
• Exploring Chat and Completion Models
• Setting up Utility Functions to Explore Model Determinism
• Using the Seed Parameter for Almost Deterministic Responses
• Tweaking Model Parameters – Temperature and Max Tokens
• Tweaking Model Parameters – Stop Sequences Top P and Penalties
• Course Summary

Using OpenAI APIs: Using Image & Audio APIs

Course: 1 Hour, 13 Minutes

• Course Overview
• Generating Images Using DALL-E Models
• Working with Image Variations and Inpainting
• Performing Audio Transcription
• Performing Translation and Text-to-Speech Conversion
• Evaluating Transcribed Audio
• Installing and Using the Whisper Model Locally
• Using Chat Completions to Interpret Images
• Course Summary

Using OpenAI APIs: Fine-tuning Models, the Assistants API, & Embeddings

Course: 1 Hour, 23 Minutes

• Course Overview
• Setting up Prompt Completion Pairs for Fine-tuning
• Creating and Executing a Fine-tuning Job
• Viewing Chat Responses from Fine-tuned Models
• Creating Assistants Programmatically
• Uploading Files to Assistants
• Creating and Using Functions to Interact with Assistants
• Creating Embeddings for Text Data
• Clustering Text Using Word Embeddings
• Classifying Reviews Using Word Embeddings
• Course Summary

Assessment:

• Final Exam: Generative AI Introduction and Overview