Introduction to Generative AI Training Certification Course

We will primarily be using popular deep learning frameworks such as TensorFlow, Keras, and PyTorch. Familiarity with Python programming is essential.

Yes, participants should have a basic understanding of machine learning concepts, proficiency in Python programming, and a grasp of foundational mathematics, particularly linear algebra and calculus.

Absolutely! The course incorporates several hands-on labs and assignments. Towards the end, participants will undertake a capstone project where they'll implement a generative model to address a real-world challenge or application.

Yes, successful participants will receive a certificate of completion by Multisoft Virtual Academy, validating their expertise in Generative AI.

Generative models learn the joint probability distribution of input and output data, and can generate new samples. Discriminative models, on the other hand, learn the boundary between classes and focus on distinguishing between different outputs given an input.

Generative AI can be used to create novel design patterns, simulate architectural structures, and even predict fashion trends, offering innovative solutions and inspirations in both fields.

Generative models can create additional training data by generating new samples that resemble the original dataset. This is useful for scenarios where collecting more real data is challenging or expensive.

Yes, if the data used to train generative models contains biases, the generated content can also reflect these biases, potentially perpetuating stereotypes or misinformation.

Transfer learning involves using a pre-trained model on a new task. Generative models trained on one dataset can be fine-tuned on a smaller, task-specific dataset, leveraging prior knowledge for improved performance.