Generative AI with Deep Learning Training

About this course

This intermediate programme, Generative AI with Deep Learning, is delivered live by an expert instructor across twelve structured modules. Each session combines theory with hands-on coding labs in Jupyter Notebooks and Google Colab, using Python, PyTorch, TensorFlow, and the broader Hugging Face ecosystem throughout. Learners progress from deep learning fundamentals all the way through to deploying production-grade generative AI systems. The programme closes with a capstone project in which every participant designs, fine-tunes, and ships a real end-to-end generative AI application complete with a FastAPI-served production API.

What is Generative AI with Deep Learning?

Generative AI with Deep Learning is the discipline of building computational models that can create new content — text, images, audio, and more — by learning the underlying statistical structure of training data. At its core it spans transformer architectures and attention mechanisms, large language models trained with causal and masked objectives, variational autoencoders that encode data into learnable latent spaces, generative adversarial networks that pit a generator against a discriminator, and diffusion models that iteratively denoise random signals into coherent outputs. Retrieval-augmented generation pipelines extend these models by grounding them in external knowledge using dense vector stores such as FAISS. Together these techniques power production systems ranging from intelligent search and document Q&A to photorealistic image synthesis and conversational AI, making them among the most practically consequential tools in modern machine learning.

Why learn Generative AI with Deep Learning now

Organisations across every industry are racing to embed generative capabilities into their products, yet the supply of engineers who can build, fine-tune, evaluate, and safely deploy these systems remains severely constrained. Practitioners who can implement transformer architectures from scratch, apply parameter-efficient fine-tuning methods such as LoRA, construct RAG pipelines with LangChain and FAISS, and containerise and serve models via Docker and FastAPI are commanding significant attention from hiring managers. Fluency with Hugging Face Transformers, Hugging Face Diffusers, Stable Diffusion, Weights and Biases experiment tracking, and responsible AI frameworks separates engineers who can prototype from those who can own a model end-to-end in production. Acquiring this full-stack generative AI skill set positions professionals to contribute immediately on high-impact teams and take on senior technical roles that previously required years of specialised research experience.

Who this course is for

• Software Engineers moving into AI — Developers with solid Python skills ready to build and deploy real generative AI systems professionally.
• Machine Learning Engineers upskilling — Practitioners who know classical ML and want to master transformer architectures, LLMs, and diffusion models.
• Data Scientists expanding into deep learning — Analysts comfortable with NumPy and pandas seeking to add VAEs, GANs, and LLM fine-tuning to their toolkit.
• NLP Engineers modernising their stack — Engineers familiar with traditional NLP wanting to work with Hugging Face Transformers, RAG pipelines, and instruction tuning.
• Computer Vision Engineers broadening skills — Specialists in image recognition ready to extend into Stable Diffusion, Pix2Pix, and multimodal generation pipelines.
• MLOps and Platform Engineers — Infrastructure professionals seeking to understand model serving, Docker containerisation, and Weights and Biases for generative workloads.

What you’ll be able to build

By the end you will have built — not just studied — the core systems of the field:

• Transformer Built from Scratch — A fully coded multi-head self-attention transformer in PyTorch, complete with positional encodings, encoder-decoder blocks, and a working sequence-to-sequence training loop.
• Fine-Tuned Large Language Model — A domain-adapted LLM fine-tuned using LoRA and prefix tuning via Hugging Face Transformers, evaluated on downstream tasks with quantitative output metrics.
• Prompt Engineering Evaluation Suite — A systematic prompt template library tested across zero-shot, few-shot, and chain-of-thought strategies against the OpenAI API and open-source LLMs.
• Conditional VAE Image Generator — A conditional variational autoencoder trained in PyTorch that generates and manipulates images through a learned, disentangled latent space representation.
• Text-to-Image Diffusion Pipeline — A Stable Diffusion inference and fine-tuning pipeline built with Hugging Face Diffusers, incorporating classifier-free guidance and evaluated using FID and CLIP Score.
• RAG-Enhanced LLM Application — A retrieval-augmented generation system built with LangChain and FAISS that grounds LLM responses in indexed documents, with retrieval quality and faithfulness evaluation.
• Production Generative AI API — A fully containerised FastAPI service wrapping the capstone generative model, tracked with Weights and Biases, monitored for drift, and optimised for inference cost.

Career paths & salary

Completing this programme prepares professionals to step into roles where generative AI capability is the primary requirement. Generative AI Engineers and Deep Learning Engineers are among the most sought-after hires across technology, media, healthcare, and financial services as organisations build first-generation AI products. NLP Engineers with hands-on experience fine-tuning large language models using Hugging Face and LangChain are in persistent demand for enterprise search, summarisation, and conversational applications. Computer Vision Engineers who can work with Stable Diffusion, GANs, and multimodal pipelines find opportunities in creative technology, e-commerce, and autonomous systems. Machine Learning Engineers and MLOps Engineers who can serve and monitor generative models using FastAPI and Docker bridge the gap between research and reliable production systems. AI Research Scientists and AI Solutions Architects who also understand responsible AI, bias mitigation, and alignment frameworks are increasingly valued as organisations mature their governance practices. Across all these tracks, the combination of theoretical depth and production fluency this programme develops is precisely what differentiates candidates in a highly competitive hiring market.