Why us
All of our programmes are full-time and in-person. We keep class sizes small. You will be surrounded by motivated peers and supported by expert practitioners.
You will receive one-on-one sessions with seasoned machine learning engineers who have worked across BigTech and research. You will read and understand cutting-edge AI papers, implement them in code, and present your solutions to a cohort of engaged and supportive peers.
Overview
Week 1. Predict HN Upvotes
Week 2. Learn To Search
Week 3. Object Detection
Week 4. Tiny Stories
Week 5. Multimodality
Week 6. Fine Tuning At Scale
Week 7. RAG
Week 8. Build Your Startup
Curriculum
Our programme is structured into a series of weekly projects, each focusing on practical applications of advanced machine learning techniques, ranging from predicting upvotes on Hacker News to building object detection models for sports analytics. Participants will engage with a variety of tasks, including text generation with Transformers, search and retrieval with Two-Tower Neural Networks, and image captioning using multi-modal models. The capstone project in the final week will allow students to apply the learned skills to a unique problem, showcasing their understanding of machine learning concepts and their ability to build impactful solutions.
The course covers data engineering, devops and deep learning and dives into key neural network architectures and methodologies such as Word2Vec, Two-Tower Neural Networks for search, and Vision Transformers (ViT) for image captioning. Participants will gain hands-on experience with complex models like YOLO for object detection and Transformer models, emphasising components like multi-head attention and custom loss functions such as those adapted for circular bounding boxes.
Throughout the course, the use of GPUs for training and inference is emphasised, alongside efficient deployment practices using Docker, Kubernetes, and Streamlit. Participants will explore Parameter Efficient Fine Tuning (PEFT) techniques such as Low-Rank Adaptation (LoRA) and soft prompting, designed to reduce computational costs while maintaining performance, especially in large language models (LLMs). Attention to deployment considerations, including mixed-precision training and distributed data parallelism, will equip participants with the knowledge to scale models effectively in real-world environments.
What you will build in practice:
Predictive model for Hacker News upvotes using word embeddings
Document retrieval system with Two-Tower Neural Networks
Object detection model with custom circular bounding regions
Transformer model for generating tiny stories
Multi-modal model for image captioning with Vision Transformers
Fine-tuned large language model using LoRA and soft prompting techniques
Tools and libraries you will use:
Cost & Eligibility
For eligible applicants, our programme is free. Apply now to find out more.