Structured lab modules from multilayer perceptron foundations to transformer architecture and AI certification capstone — delivered with honest prerequisites and instructor-led bench sessions.
Twelve-week cohort covering multilayer perceptrons, activation functions, backpropagation, gradient descent, loss functions, optimisers, and regularisation. Hands-on Python for AI and PyTorch introductory labs build the foundation for every advanced module.
Prerequisites: Python fundamentals and basic linear algebra.
Includes live bench sessions, module milestones, and completion certificate reflecting participation.
Ten-week programme on CNN architectures, convolution layers, pooling, batch normalisation, and image classification with PyTorch. Learners complete computer vision portfolio projects demonstrating model training and evaluation.
Prerequisites: NML-001 or equivalent neural network fundamentals.
Hybrid Sherbrooke sessions and live online delivery available.
Eight-week RNN and LSTM programme with attention mechanism introductions for natural language processing and sequence prediction. Covers vanishing gradients, sequence-to-sequence models, and practical NLP applications.
Prerequisites: fundamentals lab completion.
Instructor feedback on every milestone submission.
Nine-week deep dive into self-attention, positional encoding, large language model concepts, generative AI foundations, and prompt engineering. Learners implement transformer blocks and explore embeddings in professional context.
Suitable for learners who have completed CNN or RNN modules.
Intensive eight-week PyTorch engineering programme covering data loaders, training loops, hyperparameter tuning, distributed training overview, and model deployment concepts. Designed for learners ready to build production-minded deep learning pipelines.
Eight-week capstone with instructor mentorship, portfolio project development, model evaluation presentation, and professional documentation standards. Culminates in a bench presentation reviewed by faculty.
Certificate attests to training participation — not employment placement or third-party credential recognition.
All programmes include structured lab notebooks, instructor-led bench sessions, and access to cohort discussion forums. Hybrid learners may attend scheduled campus days at our Sherbrooke Notre-Dame lab for whiteboard architecture reviews and hands-on PyTorch exercises. Self-paced options provide checkpoint quizzes with optional live Q&A. Corporate cohorts receive customised project briefs aligned to organisational data contexts without crossing into AI consulting territory.
Enrolment opens on a rolling basis for most cohorts. Early registration is recommended as bench sessions at our Notre-Dame corridor campus have limited capacity. Programme advisors are available through the contact form for questions about module sequencing, prerequisite waivers, and corporate group bookings.
Speak with a programme advisor or browse delivery formats for your schedule.
Start an experimentNeuralMindLab offers vocational training in artificial neural networks and deep learning. Completion certificates reflect course participation — not university degrees, professional licensure, or guaranteed employment. Outcomes depend on prerequisites met, practice time, and individual aptitude. We are not an AI consulting agency.
