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Education & Training

AI for Scientific Data

Intensive, hands-on deep learning training designed for researchers and R&D teams, taught by the authors of the Deep Learning Crash Course textbook.

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Courses Delivered

500+

Participants Trained

Flexible

Program Duration

1:6

Mentor-to-Student Ratio

Course Textbook

Built on the Deep Learning Crash Course

A Hands-On Introduction to Artificial Intelligence

Authored by the IFLAI team, published by No Starch Press (2026). Our curriculum follows this textbook directly, providing a structured, reproducible approach to AI education for scientific teams.

Project-based learning from chapter one

Real scientific datasets throughout

From dense networks to transformers & beyond

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Modular Curriculum

From Foundations to Frontier Methods

Our curriculum is fully modular and can be delivered as anything from a single-day workshop to a multi-year course. We tailor the scope, depth, and focus to your team's needs and domain.

Participants will learn to:

Select and prepare scientific datasets for AI

Train, evaluate, and interpret neural networks

Apply modern methods — transformers, U-Nets, self-supervised learning

Advance to frontier — GNNs, diffusion models, deep RL

Build and iterate on your own models in mentored workshops

Emphasis on reproducibility and responsible AI

Foundations

Core AI for Scientific Data

Dense Neural Networks

Capturing trends and recognizing patterns: regression, classification, regularization

Convolutional Neural Networks

Processing images: augmentations, transfer learning, ROI evaluation

Recurrent Neural Networks

Processing time series: sequence labeling, forecasting, GRU/LSTM

Attention & Transformers

Processing language & images: ViT, BERT, tokenization, fine-tuning

Autoencoders & Active Learning

Enhancing data, denoising, latent embeddings, continuous improvement

Advanced

Advanced Methods & Workshops

Self-Supervised Learning

Contrastive & masked approaches for microscopy and omics

U-Nets for Segmentation

2D/3D segmentation, loss functions, class imbalance, post-processing

Representation Learning

Autoencoders + self-supervised pipelines for limited labels

In longer programs, participants pivot to mentored workshops on their own data and projects.

Frontier

Complex & Frontier Methods

Graph Neural Networks

Molecular modeling, reaction & property prediction, structural biology

GANs & Diffusion Models

Data synthesis, denoising, in-painting, generative evaluation

Deep Reinforcement Learning

Experiment design, sequential decision-making policies

Reservoir Computing

Nonlinear dynamics in physiology & lab systems

How It Works

Every program is tailored to your team. Here's what stays constant.

Flexible Format

From a single-day workshop to a multi-week course or multi-year program, we adapt to your schedule and goals.

Small Cohorts

Typically 20–28 participants with ~1 mentor per 6, ensuring personal guidance and hands-on support.

Bring Your Own Data

In extended programs, participants work on their own datasets and walk away with a working pipeline.

Ready to Upskill Your Team?

Whether you're a university department, a pharma R&D team, or an industrial research group, we tailor each program to your domain and data.