This specialization provides a hands-on pathway to mastering Generative AI techniques from foundational architectures to cutting-edge deployment strategies. Learn to build and train Autoencoders, VAEs, and GANs using TensorFlow to generate synthetic data and realistic outputs. Dive into attention mechanisms and Transformer models powering GPT and BERT. Apply RAG for improved accuracy and analyze emerging GenAI trends to create industry-ready solutions.
By the end of this program, you will be able to:
- Train Generative Models: Build and evaluate VAEs and GANs using real-world data
- Generate Synthetic Data: Use VAEs and GANs to create images and other outputs
- Apply Transformer Models: Leverage attention mechanisms in models like GPT and BERT
- Improve Output Accuracy: Use Retrieval Augmented Generation (RAG) for enhanced results
- Deploy GenAI Solutions: Translate emerging model trends into industry-ready applications
Ideal for developers, ML engineers, and AI enthusiasts exploring next-gen model development.
Praktisches Lernprojekt
Project Overview 1: Sentiment Classification on Product Reviews
Analyze customer reviews to classify sentiment as positive or negative. Learn to clean data, extract features, and build a sentiment model using machine learning in Python. Gain practical skills in NLP for e-commerce and feedback analysis.
Project Overview 2: Generating Fake Images with GANs
Build and train a Generative Adversarial Network (GAN) to create realistic images. Work on generator and discriminator models, training loops, and evaluation using TensorFlow or PyTorch. Learn key concepts in adversarial learning and computer vision.
