Part 2 - Introduction to Generative AI

Course overview

Uplatz offers comprehensive training on Generative AI Fundamentals Specialization This is video-based online course (self-paced training). You will be awarded Course Completion Certificate at the end of the course.

Generative AI refers to a category of artificial intelligence techniques and models that are designed to generate new, original content. Unlike traditional AI models that are often used for tasks such as classification, prediction, or optimization based on existing data, generative AI focuses on creating new data or content that is similar to, but not directly copied from, the input data it was trained on.

There are several approaches to generative AI, including:

1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a competitive manner. The generator creates new data samples, such as images or text, while the discriminator tries to distinguish between real and generated data. Through this adversarial process, GANs can generate highly realistic content, such as images, music, or even text.

2. Variational Autoencoders (VAEs): VAEs are another type of generative model that learns to represent input data in a lower-dimensional latent space. By sampling from this latent space, VAEs can generate new data samples that are similar to the training data. VAEs are often used for tasks such as image generation, text generation, and data imputation.

3. Transformer Models: Transformer models, such as OpenAI's GPT (Generative Pre-trained Transformer) series, are large-scale neural network architectures that are trained on vast amounts of text data. These models can generate coherent and contextually relevant text based on a given prompt or input. They have been widely used for tasks such as text generation, language translation, and dialogue generation.

Generative AI has numerous applications across various domains, including:

• Content Generation: Generating images, text, music, or other forms of creative content.
• Data Augmentation: Creating synthetic data samples to augment training datasets for machine learning models.
• Creative Design: Assisting artists and designers in generating new ideas, designs, or concepts.
• Simulation and Prediction: Generating synthetic scenarios or predictions for planning and decision-making purposes.
• Personalization: Generating personalized recommendations, product suggestions, or user interfaces based on individual preferences and behaviors.

Overall, generative AI holds great promise for unlocking creativity, enhancing productivity, and enabling new forms of human-computer interaction across a wide range of applications.