In this section of our AI Engineering bootcamp, you'll gain a comprehensive understanding of generative AI models, explore their evolution and real-world applications, and develop the technical skills to build and deploy these models effectively.

We'll cover the statistical foundations of Generative AI, delve into key architectures like GANs, VAEs, and Transformers, and examine cutting-edge tools and techniques like diffusion models and RAGs.

By the end, you'll have the knowledge and practical experience necessary to contribute to this rapidly evolving field, from understanding data distributions to implementing and optimizing your own generative AI solutions.

This module on Generative Adversarial Networks (GANs) provides a comprehensive overview of this foundational AI model, from theory to real-world applications. It begins by introducing GANs, their principles, and their evolution in generative AI.

You’ll explore GAN architecture, focusing on the interplay between the generator (G) and discriminator (D), along with the mathematical foundations that drive their functionality. Practical applications like synthetic data generation, DeepFakes, and tools like DALL-E will also be examined, alongside the challenges GANs face.

The module concludes with a hands-on case study where you’ll build and train a GAN from scratch in Python using TensorFlow. By the end, you’ll understand GANs and have the skills to implement them in practical scenarios.

In this module, you’ll master Transformers, the foundational architecture behind large-language models like GPT, powering tools such as ChatGPT. The training covers everything from basic language modeling to advanced techniques, providing a deep understanding of Transformer architecture and the attention mechanism.

We’ll explore the history and evolution of Transformers, revisit RNNs and LSTMs to understand their limitations, and examine vector embeddings and positional encodings with hands-on examples. You’ll dive into the attention mechanism, including self-attention and multi-head attention, and gain a clear grasp of the mathematics behind Queries, Keys, and Values.

The module unpacks essential components of Transformer architecture, such as layer normalization, residual connections, and masked attention, along with optimization techniques. It concludes with an end-to-end project, where you’ll build and pretrain a “BabyGPT” model from scratch in PyTorch. By the end, you’ll have a complete mastery of Transformers, the driving force behind modern language models and generative AI.

In this module, you’ll master Transformers, the architecture at the heart of large-language models like GPT, which power tools such as ChatGPT. The training provides a comprehensive journey from foundational language modeling concepts to advanced techniques, offering a thorough understanding of Transformer architecture and the attention mechanism.

You’ll explore the history and evolution of Transformers, revisit RNNs and LSTMs to understand their limitations, and work through hands-on examples of vector embeddings and positional encodings. The attention mechanism will be unpacked in detail, including self-attention, multi-head attention, and the mathematics of Queries, Keys, and Values.

The module covers critical aspects of Transformer architecture, such as layer normalization, residual connections, masked attention, and optimization techniques. It culminates in an end-to-end project where you’ll code and pretrain a “BabyGPT” model from scratch in PyTorch. By the end, you’ll have a complete mastery of Transformers, the engine driving modern language models and generative AI.

LLMs are the backbone of Generative AI, driving innovation across industries. This module provides a comprehensive guide to mastering LLMs, covering their lifecycle from data preparation to advanced fine-tuning, optimization, and deployment.

You’ll explore the evolution of language models, key applications, and notable examples like Llama and GPTs. Foundational techniques, including tokenization and embeddings, are paired with practical Python implementations. The module also covers the LLM data pipeline, from cleaning and preparation to pre-training.

Advanced topics include Parameter Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA, as well as optimization techniques such as quantization and pruning. A hands-on project fine-tuning an LLM equips you with the skills to build, optimize, and deploy cutting-edge models.