This course immerses students in the world of generative artificial intelligence (GenAI). It covers high-level browser-based tools like ChatGPT, emphasizing the Python code behind them. Neural network architectures—such as Transformers and Diffusion models—are explored. Specific applications like Text-to-Speech, Text-to-Image, and Text-to-Video generation are introduced. Classic models like BERT and GPT are studied, along with practical skills like fine-tuning and building applications. By course end, students emerge equipped to craft expert prompts, train custom models, and apply GenAI techniques to real-world challenges.
This course focuses on the principles and applications of generative AI (GenAI). It covers browser-based tools like ChatGPT and emphasizes the Python code that makes these tools work. Students will use Python to learn about Natural Language Processing (NLP) and explore the neural network architectures that are used in generative AI, including Transformer and Diffusion models. Once establishing the theoretical foundations, students will learn about specific applications and extensions such as Text-to-Speech (TTS) and Text-to-Image, Image-to-image, and Text-to-Video generation. Students explore classic models like BERT and GPT in detail, covering the original scientific papers that started the generative AI revolution. The course covers fine-tuning these and other models, building applications with LangChain, and practical exercises, including fully guided end-to-end projects, with topics ranging from branding and logo generation to voice cloning and Deep Fakes. By the end of the course, students will be able to craft expert prompts for various generative AI algorithms, train custom models, optimize model performance, and apply generative AI techniques to solve complex problems, preparing them for a job market that increasingly values and demands GenAI tools and expertise.
Prerequisites
Before embarking on the ‘Generative AI with Applications in Python’ course, it is imperative to establish a robust foundational knowledge. We suggest the following preparatory steps:
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