This course Introduction to Generative AI Studio, a product on Vertex AI, helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge.
You’ll learn Skills
- Deep Learning Fundamentals: Generative AI often relies on deep learning techniques, so you’ll likely learn the fundamentals of neural networks, backpropagation, activation functions, and loss functions.
- Generative Models: You’ll become familiar with various generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models like LSTM and Transformer-based models.
- Python Programming: Proficiency in Python is essential for working with generative AI, as most libraries and frameworks (such as TensorFlow and PyTorch) used for deep learning are Python-based.
- Data Preprocessing: You’ll learn how to prepare and preprocess data for training generative models, including techniques for data augmentation and normalization.
- Model Training: You’ll learn how to train generative models, tune hyperparameters, and monitor the training process to ensure the models converge effectively.
- Loss Functions: Understanding different loss functions used for generative models and how they impact the training process is crucial.
- Generative Adversarial Networks (GANs): If GANs are a part of the curriculum, you’ll gain knowledge about how GANs work, including the generator-discriminator architecture and techniques for improving GAN stability.
- Variational Autoencoders (VAEs): If VAEs are covered, you’ll learn about encoding and decoding data using probabilistic models and how to generate new data samples.
- Recurrent Neural Networks (RNNs) and Transformers: If text generation is a focus, you may learn about RNNs and Transformer-based models for sequence generation tasks.
- Natural Language Processing (NLP): If the course includes NLP applications, you’ll learn about tokenization, word embeddings, and language modeling.